The aim of the present study was to develop a pictorial questionnaire for the assessment of health-related quality of life (PictoQOL) and to examine its content validity and usability across three exemplary population groups of different origin residing in Germany . A mixed-methods design combining qualitative and quantitative methods was used, comprising 6 focus group discussions with a total of 17 participants, 37 cognitive interviews and a quantitative pretest with 15 individuals. The PictoQOL consists of a pictorial representation of a total of 15 different situations. Using a visual Likert scale, respondents indicate how much each situation applies to them. Some representations proved to be culturally sensitive and were adapted. Respondents found the use of an additional graphic layer in the form of symbols in addition to pictures helpful for interpretation. The PictoQOL is considered to allow a more accessible assessment and better comparability of HRQOL across different population groups regardless of their literacy level. It is therefore considered to be superior to existing instruments for routine use in health research and practice. Future studies need to examine its convergent and factorial validity.  Health-related quality of life (HRQOL) is a frequently studied patient-reported outcome in health research . ExampleHRQOL is usually assessed by means of standardized self- or interviewer-administered questionnaires, e.g., from the SF-36, EuroQol or WHOQOL family ,3. TheseOne strategy to address the aforementioned challenges can be the use of language-independent questionnaires which are based on pictograms. Such instruments can be applied across different population groups with different language skills and require no or only little re-adaptation ,18,19. ACurrently, questionnaires which are at least to a certain degree based on pictograms or pictures are occasionally used to assess psychological constructs such as anxiety or various developmental disorders in children and adolescents ,25,26,27Most dedicated HRQOL questionnaires, including those used in assessments of children and adolescents, are language-based or use pictures and illustrations only as visual elements . Based oTo the best of our knowledge, however, no instrument is currently available in which the assessment of HRQOL is conducted mainly language-independent. The aim of the present study was to develop such a pictorial questionnaire for the assessment of health-related quality of life (PictoQOL) and to examine its content validity and usability in three selected population groups residing in Germany. These are non-migrants, Turkish migrants as one of the largest population groups with a migration background in Germany  and ArabFor the development of the PictoQOL, a participative qualitative design consisting of focus group discussions and cognitive interviews was used. All authors are experienced in qualitative interview studies and have had specific professional training. PB is a professor for health services research and an experienced researcher with a doctoral degree in public health. YYA is an experienced researcher with a doctoral degree in public health. SÖ is an experienced researcher with a doctoral degree in sociology. FE, TA, DP, MM and DW are experienced research assistants and doctoral students. KA and MI are trained research assistants with experience in qualitative research.Interviewees were recruited from the general population based on theoretical sampling aiming to collect qualitative data with the broadest possible range of information ,43,44. AWhile we had originally planned to conduct 2 focus group discussions with 9 participants each, we changed the design to 6 focus groups with 2–4 participants each in order to allow a more comprehensive discussion process. For the cognitive interviews, we estimated the necessary sample size to be 45 based on previous experiences in the field , resultiFollowing the development phase, a quantitative pretest of the questionnaire was conducted using a convenience sample of 15 individuals aged 18 years or older.Pictorial items were designed with the help of 2 × 3 sequential focus group discussions composed of the three aforementioned population groups. This participatory approach aimed to ensure that the target group is involved in the development process from an early stage on to increase usability, comprehensibility and acceptance of the instrument . Based oTo stimulate discussion and to guide the creative process, the focus group participants were provided with ‘visual cues’ for each of the eight dimensions of the SF-36 measurement model ,50. ThesDuring the first three focus group discussions, participants discussed these cues in terms of comprehension, perception of the images, congruence between images and item contents and possible alternative depictions. On the basis of the results, a pool of potential pictorial items was composed, which was further reduced to a draft version of the questionnaire in the second round of the focus group discussions.All focus groups were carried out in reserved discussion rooms at the university and took between 50 and 80 minutes. Focus group discussions in German or English were conducted by FE, TA, DW and DP. Focus group discussions in Turkish and Arabic were conducted by FE, MI and KA. All discussions were audio-recorded and supplemented by field notes to provide additional context.In the development of the PictoQOL, a great emphasis was placed on a diversity-sensitive design of the pictorial items which not only considered cultural and religious aspects but also took into account that other characteristics of social diversity such as gender, age and socioeconomic status can be associated with particular needs and expectations  which muThe draft of the pictorial items was subsequently further refined by means of cognitive interviews with 37 individuals . The Survey Response Process Model  was usedUsing a convenience sample of 15 individuals, we tested the draft of the final questionnaire for comprehensibility. Respondents were recruited through snowball sampling and were not among the participants who already took part in the focus group discussions and cognitive interviews during the development of the PictoQOL. A preface with information about the study, contact details and text boxes for feedback on each item and a form for sociodemographic information were added. The focus group discussions and cognitive interviews were audio-recorded, transcribed, translated into German and examined by means of qualitative content analysis. In addition, field notes were taken during the focus groups to complement missing information on selected images. In the cognitive interviews, the individual items (pictures) represented the unit of analysis. For this purpose, the responses obtained using the think-aloud approach and verbal probing were examined separately for each item . For theA total of 17 individuals, 8 of whom were women, participated in the 6 focus group discussions. Participants were between 23 and 50 years old, comprising non-migrants, Turkish migrants and Arabic-speaking migrants. An overview of their sociodemographic characteristics is provided in Following the development of a draft, the items were further refined in cognitive interviews with 37 individuals —with some of the necessary adjustments described above—can be illustrated well using pictures, particularly the middle categories  are difficult to visualize. This is particularly the case for complex dimensions such as general health, role functioning and social functioning. We therefore dismissed that approach and decided in consultation with the study participants to only illustrate the endpoints with pictures and to apply a graphical 5-point Likert scale in between  needed to be adjusted. While detailed backgrounds were seen as helpful elements to identify social interaction, physical activities and tasks, participants preferred a higher focus on gestures and facial expressions for items addressing emotions and individual reactions to external stimuli. Depictions of sadness, fear, anger and pain were seen as easier to identify when the images focused on the face and body of the depicted person. High levels of detail of the background were even regarded as distracting in some cases and negatively affected recognition instead of improving it. In contrast, items focusing more on interaction with physical surroundings or social roles required a detailed background to be recognizable. The use of a recurring character in the images was considered helpful for identification. Using the same character helped participants to understand that the items represented different questions or inquiries directed at them. When asked about preferences in skin color, gender and age of the persona, participants reported no specific preference. To identify potential differences in perception, we tried out versions with more clearly gendered personas and versions with a mostly androgynous persona. We found that when using an androgynous persona, participants often interpreted these as someone of their own gender, which is why an androgynous character has been used for the final version of the questionnaire.Interpretation or preference of visual cues or pictorial elements in some cases differed between participants from different cultural backgrounds. While participants mostly had similar interpretations of the depictions, preferences and perceptions for some elements varied. These differences were mostly related to symbols representing specific concepts or ideas, such as a red cross or a red crescent as illustrations or symbols for healthcare and medicine. In contrast, other symbolic representations were understood by all participants, e.g., the rod of Asclepios as a visual representation of health and healthcare. While these differences in perception were not observed for facial expressions, posture and gesture of depicted individuals in some cases led to differences of interpretation. While some participants interpreted stylized lightning bolts as indicators of pain, other participants did not understand these symbols well. Turkish participants also indicated that facial expressions alone were insufficient to depict pain, but instead placing an individual’s hands on the hurting body part was regarded an important indicator of pain.In addition, the majority of participants generally relied on facial expressions alone to identify specific emotions such as fear or sadness, while Arabic-speaking and Turkish participants put more emphasis on posture. While participants placed different emphasis on the importance of facial expression and posture, preferred postures and facial expressions for specific emotions were similar among the participants. In addition, for some depictions, religious beliefs influenced the interpretation of certain aspects. For example, a visual representation of a person sleeping on the side with a relaxed face and their hands under their cheeks was generally seen as a good representation of good sleep quality, while two Muslim participants felt that the person needed to sleep on the right side to represent good sleep and to stay in accordance with the teaching of the Prophet Muhammad.In addition, most participants regarded simplified symbols in addition to the main pictograms helpful. In some cases, these symbols were considered to serve emphasizing the focal point of the respective image, while in other cases the symbols were seen as helpful to identify the differences between the positive and negative endpoints. For example, participants found the use of arrows of different length with equidistant markings to be helpful symbols to represent distance. Participants specifically found symbols helpful when they clearly reflected the opposite end points of the scale. For the pretest, we received responses from 15 participants with different socioeconomic characteristics see . PossiblHRQOL is an important patient-reported outcome and a relevant determinant frequently studied in health research . StandarWe designed the PictoQOL with the intention to facilitate an entirely non-verbal assessment of HRQOL. As such, the questionnaire is different from other approaches to pictorial questionnaires developed in the past ,33,35,36Whereas some modules of the QoL-ME, which has been developed for the assessment of QOL in patients with severe mental health conditions , are alsMore complex concepts underlying some dimensions of the PictoQOL, however, still posed some challenges, and we needed to reduce the complexity and scope of some dimensions. This, for example, was the case for role functioning. Future research into the convergent validity of the PictoQOL needs to examine whether these dimensions are still satisfactorily correlated with the respective dimensions of the SF-36.In the development of the PictoQOL, a great emphasis was placed on the diversity-sensitive designs of all questionnaire elements. It is well known that non-verbal communication may differ between cultures  and thatThe PictoQOL was designed as a paper-based questionnaire to be completed by the individuals themselves in order to ensure the lowest possible threshold for application in clinical practice. However, a later transfer into an interactive computer-/smartphone-assisted version is possible given the benefits and potential associated with the web- or app-based assessment of self-reported information ,73. Strengths of the study are its participatory approach, the use of different techniques of cognitive interviewing and its focus on three different population groups. However, some limitations need to be considered as well. Although the items performed well in cognitive testing and can be regarded content valid in the three population groups studied, the perspective is still limited. Future research needs to examine whether the items are also comprehensible in other cultural and ethnic groups and also needs to investigate the convergent and factorial validity as well as the measurement equivalence of the PictoQOL. We used a theoretical sampling approach for the recruitment of participants for focus group discussions and cognitive interviews. Still, the maximum age of respondents was only 63 years, as it was particularly difficult to recruit migrants of older age. Future studies must therefore also examine the comprehensibility of the pictorial items in the age groups of individuals 65+. Furthermore, recruitment of individuals for the pretest was only based on a convenience sample and only allows exploratory insights into the performance of the questionnaire which must be complemented by future validation studies based on random samples.The PictoQOL has the potential to facilitate an assessment across different population groups irrespective of their linguistic and literacy skills and to allow an anonymous and efficient self-administered assessment of HRQOL also among population groups who usually cannot be surveyed by means of standardized interviews and who therefore are not included in surveys. Future studies need to examine its convergent and factorial validity as well as its measurement equivalence across different population groups. Provided those requirements are met, the PictoQOL would allow valid comparisons of an important quality indicator in healthcare in population groups which currently can hardly be studied by self-administered population-based questionnaires because of limited reading and writing skills . It woul


Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor. Smart and precision farming can improve crop yield and quality with the ability to predict and prevent diseases, providing flexible and efficient solutions, using unmanned vehicles, drones, and sensors that allow constant monitoring of the crop . Thus, sThe use of visible (VIR) and infrared (NIR) imaging provides information on crop health and growth stage. During the photosynthesis process, chlorophyll molecules of plants absorb blue and red light and reflects green light. On the other hand, infrared light penetrates the inner part of leaves reflecting infrared energy. Since leaf spectral reflectance changes with plants growing, affection of diseases and pest infections, employing these images allows adequate monitoring of crops . TechnolImage processing allows the combination of different sensor techniques, which leads to some challenges. Since the images are taken from two different cameras and at various time periods, rotations and translations between them are unavoidable. In consequence, it is a necessary process to unify acquired data for further analysis. In this case, the image matching process is adequate to detect similarities between them . This prAdditionally, image processing also enables monitoring and estimation of crops yield from vegetation indices . In addiThere are several comparative studies of image features detectors and descriptors behavior where the aim of the work is to find the most suitable one for images matching. For example, Noble  comparedFeature matching was performed by fast library for approximate nearest neighbors (FLANN) and exhaustive search, commonly known as Brute Force (BF), methods on a crop taken by the two cameras at different angles. Rondao et al.  benchmarFurther studies in the state-of-the-art modified feature detectors and descriptors to improve its performance, integrating georeferenced points taken by UAV and SURF descriptor to automate the acquisition from RGB and hyperspectral images .Several works have been carried out on the analysis of image feature extraction methods using feature descriptors and detectors in agricultural applications. Saleem et al.  conducteThis paper is focused on a benchmarking of different state-of-the-art feature detectors and descriptors: binary robust invariant scalable keypoints (BRISK), features from accelerated segment test (FAST), speeded up robust features (SURF), scale invariant feature transform (SIFT), oriented FAST and rotated BRIEF (ORB), binary robust independent elementary features (BRIEF), fast retina keypoint (FREAK), KAZE, and AKAZE. Furthermore, two matching methods available in open computer vision (OpenCV) library, in Python language, were used: fast library for approximate nearest neighbors (FLANN) and Brute Force (BF). SIFT is a feature detector and descriptor invariant to image scale and rotation, that employ a 3D spatial histogram of the image gradients for feature description. SURF is a feature detector and descriptor very similar to SIFT, that applies an approximate Gaussian second derivative mask to an image at many scales for feature detection. FAST is a corner detection method that compares pixel intensity to identify key points. BRISK is a detector and descriptor robust to image rotation, based on a scale-space FAST method. BRIEF is a binary feature point descriptor that employs simple tests using intensity difference to describe key points in a pair of images. ORB is the result of using the FAST method to detect key points and the BRIEF detector with some modifications. FREAK is a binary keypoint descriptor which employs a highly structured pattern that mimics the human eyes. KAZE is a feature descriptor that aims to detect and describe 2D image features in a non-linear scale space, using the Hessian matrix. AKAZE is the accelerated version of KAZE, using non-linear scale space based on anisotropic diffusion. The used dataset is a set of visible (VIS) and near-infrared (NIR) spectral images of different cropland and rice stages.A set of 96 images was acquired using two RGB and NIR cameras located on two 8-meter high towers, used as part of a field phenotyping system. In total, 48 RGB images from the visible spectrum were acquired with a Nikon D300s SLR camera and 48 NIR images of the same scenes were acquired with a modified Nikon D80 SLR camera. The latter was modified by placing an IR-85 high-pass filter  over the CCD sensor to capture in the NIR wavelength regions. For this study, a randomly selected subset of 40 images was taken from the total number of photos, a sufficient number to statistically validate the results obtained. Thus, the dataset consists of two groups of 20 images, corresponding to the NIR and VIS spectra, with sizes of 3872 × 2592 and 4288 × 2848 pixels, respectively. The images were scaled to 774 × 518 pixels to facilitate handling and reduce computational cost. As seen in To develop the comparative study, a process of selection of points of interest was initially carried out and then a technique was used to describe them using the algorithms described below.SIFT: This method detects the key points by applying a Gaussian filter to smooth the image while rescaling it. In this way, a pyramid is generated with the original image at the first level. The key points are then detected around a nvariant ;SURF: The keypoint detection process of this algorithm is very similar to SIFT, however, the SURF is based on a Hessian matrix, which is generated by convolution of the Gaussian second-order derivative with image pixels by detecting the keypoints using a criptors ;FAST: This algorithm aims to find points of interest in an image and is specialized in corner detection. To find the points of interest it analyzes each pixel p, comparing its intensity with that of the four pixels located in a circle of radius 3, forming a cross with the center p. If at least 3 of them are darker or brighter than p, p is compared with the 16 pixels located on the edge of the circle. If at least 12 of them are darker or brighter than p, and are consecutive p is identified as a key point [ey point ;BRISK: Is a detector and descriptor robust to rotational changes, based on a scale-space FAST method introduced by [n octaves n samples is used, usually k is calculated. Afterwards, two subgroups are defined, the first one formed by pairs of samples separated by a short distance S and the other one by pairs separated by a larger distance L. In this way, the estimate of the direction of k is obtained. For obtaining the rotation invariance, the sampling pattern rotated by k is used. The description of point k is formed by the bit vector duced by . It takeordingly . After o measure ;BRIEF: Is a binary feature point descriptor that employs simple tests using intensity difference to create binary feature vectors that effectively describe key points in a pair of image patches. Before executing binary tests, images patches are smoothed using a Gaussian kernel at a pixel level, reducing noise sensitivity. The obtained binary strings with BRIEF only require between 128 and 512 bits, a relatively few number of bits compared with other state-of-the-art feature descriptors. The Hamming distance is employed for evaluating the ranking of descriptors, instead of the L2 distance, since it is easier to calculate. Although construction and matching for this descriptor is faster than other state-of-the-art ones, does not provide rotation invariance, but tolerates small amounts of rotations. Since BRIEF does not make features detection, any fast detector, such as CenSurE (Star) or FAST, can be used with this descriptor, but should not be used with SURF as it would negate BRIEF’s speed advantage [dvantage ;ORB: This method results from a fusion between the FAST keypoint detector and the BRIEF descriptor with some modifications. ORB employs the FAST method to detect the key points and implements the Harris corner detector to find the main N points at each level of a multi-scale image pyramid, thus seeking to be invariant to scale change. Orientation invariance is obtained by calculating the intensity centroid of a circular patch centered at the keypoint. The orientation is given by the direction of the vector located between the pixel, located at a corner, and the intensity centroid. ORB uses BRIEF as its feature descriptor doing a ’steer’ BRIEF according to the orientation of the keypoints. To construct the feature set, the result of n binary tests at the points of interest  is taken and the orientation of the patch lculated ;FREAK: Is a binary keypoint descriptor which employ a highly structured pattern that mimics the human eyes saccadic search for binary tests. A cascade of binary strings is computed by efficiently comparing pairs of image intensities. The sampling method involves a smoothing process with a Gaussian kernel using a circular retinal sampling grid. The radius of the circular grid represents the size of the kernel standard deviation, corresponding with the distribution of receptive fields over the retina. This descriptor is very similar to BRISK, but unlike this one, an exponential change in size and the overlapping receptive fields in the topology, that captures more information is handled. The added redundancy allows the use of less receptive fields, which is a known strategy employed in compressed sensing (dictionary learning). FREAK also follows ORB’s approach to learn the best pairs from training data by maximizing variance. It takes advantage of this structure to further speed up the matching using a cascade approach: more than 90% of the candidates are discarded with the first 128 bits of the descriptor if the distance is smaller than a threshold. Finally, FREAK compensate rotation changes measuring the orientation in a similar way to BRISK. However, instead of using long distance pairs, a predefined set of 45 symmetric sampling pairs is used [ is used ;KAZE: The algorithm consists of detecting and describing 2D features in a non-linear scale space to obtain better distinction and location precision. From an input image, a non-linear scale space is constructed by discretizing the scale space in logarithmic steps arranged in a series of octaves and sublevels while maintaining the resolution of the original image. Then, convert the set of discrete scale levels in units of pixels  to create a fine and coarse pyramid frame. In this way, a set of filtered images is obtained to build a non-linear scale space. For feature detection, a Hessian determinant and a modified version of the local difference binary descriptor (M-LDB), which exploits the gradient and intensity information of the non-linear scale space, are used [are used .BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were used as features detectors and descriptors. Furthermore, BRIEF, BRISK, and FREAK methods were employed for the features description and FAST algorithm was implemented for keypoint detection. To make a more complete study, the techniques mentioned before, which were developed with the aim of reducing the computational cost of finding matches, were included. For the sake of simplicity, the combination of some detectors and descriptors are named as follows: FAST + BRIEF, FAST + BRISK, and FAST + FREAK. For the FAST + BRIEF method, the threshold used was 25. Meanwhile, for the other approaches, default parameters were employed. These state-of-the-art methods are described below:For the evaluation of the points of interest, two color spaces were used: the standard RGB image and one where the intensity information of the image can be isolated in a single channel. The CIE L*a*b* color space was used for this purpose. The implemented methodology is shown in The process consisted initially in obtaining the key points and descriptors in the NIR image and in the corresponding image of each channel . Matching features were then found by Brute Force (BF), using Hamming and Norma L1 distances for matching, as shown in To determine the performance of the detectors and feature descriptors, the twenty points of interest with the best match between each pair of images were obtained and the execution time measured. The number of correct matches between the twenty points of interest was also observed. Only twenty points were taken, as the number of matches generated from the detectors and feature descriptors was too high see a, which The average number of matched keypoints between the NIR image and each color component of the VIS images in RBG and CIE L*a*b* spaces and the average run time were also obtained. The results achieved with the image in Considering the average number of matches found on RGB and CIE L*a*b* images from dataset, the performance of each feature descriptor and detector was evaluated, as it is illustrated in As shown in In Execution time was also used to evaluate performance of feature descriptors and detectors, as can be seen in The To evaluate the performance of the methods, the accuracy of the twenty most significant match points obtained between each pair of images with the different techniques was also obtained. The precision is given by the number of correct matches over the total number of matches . This meTherefore, in addition to the average match results shown in Since the green channel showed the best performance, in terms of correct matches, for most of the feature detectors and descriptors, the matching methods exhibited similar results. The major differences could be seen using ORB see , FAST + In addition, some differences can be seen in the results for other methods, like BRISK see , FAST + In all cases, the performance is slightly better using the BF method, because fewer matching errors were made. Finally, almost no differences are observed in the cases of SIFT see  and AKAZMoreover, from Based on the The results of this work allowed determining the best descriptors and matching methods to fuse RGB and NIR images of rice crops of different phenotypes, planted in small parcels, in order to derive different vegetation indices to monitor the health and productivity of rice crops. These methods can also be extended to any crop and other low-cost phenotyping platforms to obtain better crop information and select resource-efficient genotypes.Image processing techniques for precision agriculture have shown great potential for applications, such as crop monitoring. Since different types of sensors and multiple feature extraction methods can be used, a comparative analysis of various feature descriptors and detectors using NIR and VIS images of rice crops was performed in this work. The results showed that the fastest algorithm was the ORB implementation, but it also had the worst performance in terms of feature matching. SIFT and FAST showed the best matches using FLANN and BF matching, respectively, on green channel images. In turn, the method that presented the highest performance in both time and number of correct matches was the combination of the FAST feature detector and the BRISK descriptor. Therefore, the various results obtained justify the need to analyze different descriptors and feature detectors to find the best performance in NIR and VIS images of rice crops. As future work, it is intended to expand the database, compare other available descriptors and detectors and apply different matching methods. Finally, possible combinations of detectors and descriptors should be tested to study their performance depending on the used database.The RGB images will bring good spatial resolution to monitor the crops at different scales, but it attains more information than RGB alone when it merges with NIR images. The methods or techniques developed from this paper will be helpful to the researchers to combine RGB and NIR images to estimate several vegetation indices to monitor crop health and productivity at different time points. Unfortunately, the hi-tech multispectral cameras are expensive, and its expensive need drones to monitor the larger areas. Still, when the experimental site is small, it is unnecessary to use the drone. Instead, cheaper RGB cameras with NIR filters can be attached to capture multiple field images and then can be merged. These low-cost cameras can be attached to the towers or moving ground vehicles to capture many high-resolution RGB-NIR images around the plots can be easily integrated using advanced fusion techniques. In this paper, we used small plot rice RGB and NIR images collected from phenotowers around the field, demonstrating efficient methods to fuse images accurately and quickly. The merging techniques employed in this research will be more beneficial to rapidly merge RGB and NIR images to derive different vegetation indices to monitor the health and productivity of rice crops. These methods can also be extended to any crop and other low-cost phenotyping platforms to obtain better crop information and select resource-efficient genotypes.


A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings. In December 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China  data, b) statistical, mathematical and decision-analytic models , and c) a set of available decisions, interventions or strategies with their consequences described through a utility or loss function (decision-making framework) and the related tradeoffs. General international guidance on these assessments and decision analysis is implemented in a country-specific manner, mainly by Health Technology Assessment (HTA) organisations  and modeling, up to communication and decision making and to provide recommendations related to these areas. We discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their application in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches, and the specific data requirements that are essential for the interpretation of results and a successful interdisciplinary collaboration. Model types less known to statisticians, such as decision-analytic models, still require statistical thinking. In particular, functional relationships and input parameters for these models are often provided by statisticians and epidemiologists. Our target audience, therefore, is broad. It includes data scientists—such as statisticians—mathematicians, physicists, epidemiologists, economists, social and computer scientists and decision scientists.The paper is organized as follows. In Sect. a priori information  on the right individuals .Transparency: The data collection process must be transparent in terms of origin, time of data collection and nature of the data. Transparency is a requirement for peer-review processes to ensure correctness of results and for an adequate modeling of uncertainties.Quality standards: Data are well suited for policy making requiring general overviews and spatio-temporal trends if local data collection follows a clear and uniform definition of what is recorded and how it has been recorded. Standardization includes, for example, the harmonization of data processes, adequate training of the persons involved in the collection, and monitoring of the processes.Trustworthiness: To place trust in the data, these must be collected and processed independently, impartially and objectively. In particular, conflicts of interest should be avoided in order not to jeopardize their credibility.Sources of error: Most data contain errors, such as measurement errors, input errors, transmission errors or errors that occur due to non-response. With a good description of data collection and data processing (see ‘Transparency’ above), possible sources of error can be assessed and incorporated into the modeling for the quantification of uncertainty and the interpretation of results.Timeliness and accuracy: Ideally, data used for policy-making should meet all quality criteria. However, information derived from data must additionally be up-to-date, and some decisions  cannot be postponed to wait until standardized processes have been defined and implemented, and optimal data have been collected. The greater uncertainty in the data associated with this must be met with transparency and with great care in its interpretation.Access to data for science: In order to achieve the overall goal of evidence-based policy making, it is important to make good data available as a resource to a wide scientific public. This allows for the data to be analyzed in different contexts and with different methods and enables the data to be interpreted from the perspective of different social groups and scientific disciplines.These eight aspects are included in the European Statistics Code of Practice  selection bias would come from accompanying information on asymptomatically infected persons gained through representative studies. Other information to mitigate selection bias comes from the number of tests and the reasons for testing, but these are not appropriately reported in Germany. Both problems yield biased regional incidences. Hence, modeling based on these data may cause misleading results and has to be considered carefully. Additionally, the data generating process may be subject to informative sampling . Then we denote A on Y is now present, if Where randomized experiments are not possible and observational data is used instead, causal conclusions are harder to draw. In order to get valid estimates in this situation, a common approach is the counterfactual framework by Rubin . For simMathematical models and simulations can also be used to understand and explain dynamic patterns. Examples are simulation studies for public health interventions such as lockdown and exit strategies, where general consequences of different measures can be compared. Seemingly simple simulation models have played an important role in communicating the dynamics during a pandemic. In these models, assumptions about the system that generates the data and  relationships are made. The (mis)match between data and model then provides insights that can be used as basis for decisions. However, this procedure does not establish causal relationships in the statistical sense described above.The main challenge in modeling for explanation is good communication, irrespective of whether the model is based on statistical or mathematical approaches. Therefore, we consider standards for good communication in detail in Sect. In statistical prediction models, the modeler can choose from large toolboxes in (spatio)-time-series analysis as well as statistics and machine learning (ML). Examples cover simple but interpretable ARIMA models  growth that may occur but the fact that the properties of the processes that describe spatio-temporal changes are a function of time themselves. This is due to the fact that the behavior of the people continuously changes the properties of the system that we are trying to understand and make predictions for. This contrasts with other natural systems, like the current weather, and most systems in the engineering and physical sciences.S, infected I, and removed population R (either by death or recovery), the contact rate t denotes the time point, and N is the total number of individuals in the population, i.e., Simple infectious disease compartmental models can be described by the stock of susceptible E, adapted models such as SEI, SEIR or SEIRS, depending on whether the acquired immunity is permanent or not can be applied  is not exactly predictable. However, there are many possible types of stochastic epidemic models Britton .Agent-based models (ABM) Agent-based modeling as an alternative approach uses individual-level simulation  Discrete event simulation is an individual-level simulation  models (STM) could be applied. STMs allow for the modeling of different health states and transitions between these states and thus also for repeated events. They are applied when time to event is important. If the decision problem can be represented in an STM “with a manageable number of health states that incorporate all characteristics relevant to the decision problem, including the relevant history, a cohort simulation should be chosen because of its transparency, efficiency, ease of debugging and ability to conduct specific value of information analyses.”  of the decision maker. For each alternative, the patients’ paths, which are determined by chance and that are outside the decision maker’s control, are then described by chance nodes. At the end of the paths, the respective consequences of each path are shown. Consequences or outcomes may include symptoms, survival, quality of life, number of deaths or costs. Finally, the expected outcomes of each alternative choice are calculated by taking a weighted average over all pathways  states and transitions between these states. Time is represented in time intervals. Transition probabilities, time cycle length, state values (“rewards”) and termination criteria are defined in advance. During the simulations, individuals can only be in one state in each cycle. Paths of individuals determined by events during a cycle are modeled with a Markov cycle tree that uses a set of random nodes. The average number of cycles in which individuals are in each state can be used in conjunction with the rewards  to estimate the consequences in terms of life expectancy, quality-adjusted life expectancy, and the expected costs of alternative interventions or health technologies. There are two common types of analyses of state-transition models: Cohort models (“Markov”)  Similar to decision trees and state-transition models, health outcomes and costs of alternative health technologies can be assessed. In addition to these outcomes, as mentioned earlier, performance measures can be calculated as additional information for decision makers, and the impact of scarce resources on costs and health outcomes can be evaluated  Agent-based models as introduced earlier, have been used for decision analysis, for example for cost-effectiveness analyses in health care in the recent years  deaths avoided and the harms of vaccination in terms of  potential side effects.Alternatively, the tradeoff of different school closure strategies in a pandemic  weighting benefits in terms of  deaths or hospitalisations avoided and lost education time should be considered. In general, two or more interventions can be compared in a stepwise incremental fashion . These standards are based on commonly accepted core quality principles and values such as accuracy, relevance, timeliness, clarity, coherence and reproducibility. For measures to restrain and overcome an epidemic effectively, communication among experts that follows highest professional and ethical standards is not sufficient. In a democratic society, policy measures can only be implemented if they are accepted by the wider population. This puts high demand on skills associated with communicating statistical evidence on the side of scientists, governments and media, and a citizenry able to understand statistical messages.For data analysis and modeling to have an impact as a component of decision making, appropriate reporting and communication is key. There are numerous standards and guidelines for study planning and statistical reporting in the numerous application areas, such as the ESS standard for quality reporting, the CONSORT, PRISMA, CHEERs guidelines and others . In addition, much of the data comes from observational studies, which usually makes a robust causal attribution problematic. As many of these phenomena cannot be studied other than by observation , causal attribution might be achieved as a scientific consensus opinion among scientists from the relevant disciplines that understand the complexity of the models and the subject matter studied. Visual representations take a central position in public communication and aim to represent the corresponding dynamics and contents in a quickly understandable way. Usually either time-dependent parameters or data with a spatial reference are visualized. For spatially distributed data, choropleth maps are predominantly used, in which administrative regions defined by the responsible health authorities are colored according to the distribution density of the infection figures or variables derived from them see Fig. . Their vFor time-dependent parameters, different variants of time series diagrams are used, predominantly line and column diagrams. The use of logarithmic scales in time series diagrams should be evaluated with caution  or data from different sources with different levels of evidence can be used to populate the model . In addition, several modeling approaches may be applicable for one research question . We recommend clear communication about 1) the purpose of the model, 2) how the model uses data, 3) the database or additional assumptions and their evidence basis, and 4) risks and uncertainties. If applicable, several modeling approaches should be applied. As a result, the model best fitting the data would be selected or different modeling approaches could provide insights into uncertainty, like in national forecasting consortia  and nowData Aquisition: Models require data from various sources, and different modeling approaches allow data transformation and synthesis from different sources. Data aquisition for scientific evaluations requires a further improved infrastructure to speed up model development and to parameterize models with high level evidence including vaccine effectiveness in real time. We recommend the creation of a central national DataLab, collecting data in a unified way and linking data from different sources as well as enabling accessibility for experienced users. With regard to data sharing, it is important that this is manageable from a practical point of view in terms of the time frame and resources needed.Transparency: In the wake of the COVID-19 pandemic, reporting of infection numbers and derived epidemiological indicators boomed, demonstrating with dramatic clarity the knowledge gap between experts, policymakers and the public. To increase the acceptance of decisions and associated measures, all steps in the decision-making process must be disclosed. We recommend a transparent decision-making process and communication of this process starting with the data, continuing with the choice of models, relevant perspectives, outcomes or metrics for several outcomes and an explicit discussion of considered tradeoffs  are examples for existing networks that can be built upon.Statistical Literacy / Data Literacy: The COVID-19 crisis brought into the general public’s awareness that our social interaction and political decisions are essentially based on data, modeling, the weighing of risks and benefits, and thus on probability estimates, expected values and incremental harm-benefit ratios. Clearly, we need additional efforts to promote statistical or data literacy at all levels of society. The ability to critically evaluate and interpret data and to critically reflect on model outcomes serves to promote maturity in a modern digitized world. We recommend promotion of statistical literacy to be intensified at all levels of education  following the Data Literacy Charta , a network of 13 statistical associations and the German Federal Office of Statistics


This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results. The oil and gas industry encompasses multiple highly complex services and facilities that involve exploration, production, and refinement of petroleum products. These services often have multiple facilities that span large distances. Transportation of hydrocarbons, often in fluid form, relies heavily on pipelines due to the large distances. Pipelines are preferred as they are the most cost-efficient and safe mode of transport for oil and natural gas ,2. It isAmong various types of corrosion, uniform corrosion, pitting corrosion, and erosion corrosion are some of the most common types that occur in pipelines . UniformBased on the DNV-RP-F101 (DNV) assessment guideline for corroded pipelines, corrosion defects can be categorized into three categories: are single defect, interacting defect, and complex-shaped defect. A single defect is a defect that is sufficiently isolated from neighboring defects such that there is no interaction between them. Its failure pressure is independent of the other defects that are present in the pipeline. Interacting defects are defined as two or more defects aligned in the longitudinal or circumferential direction that interact with one another. The resulting failure pressure is lower than that of the individual single defects. A complex-shaped defect results from the combination of different colonies of interacting defects or a single defect . Since iConventional residual strength assessment methods generally result in conservative pipeline failure pressure prediction due to the assumptions and safety factors. This results in unnecessary pipeline maintenance and repairs. However, with the use of computer-aided failure analysis methods such as the finite element method (FEM), the accuracy of pipeline failure pressure prediction could be enhanced . HoweverOver the years, various methods have been developed to assess the failure pressure of corrosion-affected pipelines. This effort was driven by the need for an accurate failure pressure prediction method in the industry. Fitness-for-purpose analysis of pipelines used in the oil and gas industry requires detailed technical assessment of a defect to ensure that the structure can serve its purpose as long as the failure conditions are not reached . In the The ASME B31G method is based on the NG-18 equation and is one of the methods that is commonly used in the industry. This method assumes the defect idealization based on the length of the corrosion. Short corrosion defects where L ≤ t bottom .By redefining the Folias factor and flow stress equations of the ASME B31G method, the modified ASME B31G method was developed. In this method, an arbitrary shape correction factor is applied instead of the parabolic area assumption. The factor 2/3 was replaced with 0.85 in the failure pressure prediction equation as presented in t. This method represents the corrosion defect region with a river bottom profile that enables a failure pressure prediction with greater accuracy using the discrete method [Besides, the RSTRENG method, also known as the effective area method, is used in assessing defects up to 0.8 e method ,12. These method .materia1, is determined using Equation (2). Combining Equations (1) and (2), Equation (3) is formed, which allows for the failure pressure prediction of a pipe with a single corrosion defect subjected to internal pressure and external stress.However, the conventional corrosion defect assessment procedures result in predictions that are conservative due to the incorporation of safety factors in the calculations ,15. WhenTraditional approaches such as the Monte Carlo simulation (MCS) method with the help of a nondestructive examination system are used to predict the probability of failure of the pipelines. The MCS is based on the concept of numeric sampling assisting in developing probabilistic models . In esseIn recent years, machine learning and finite element method (FEM) have been researched to replace the time-consuming methods and conventional codes. The failure pressure predictions obtained by utilizing these tools are more accurate and less conservative compared to the conventional approaches . In thisGurney  defined The architecture of an ANN also depends on the type of data and desired output. Some of the commonly used ANN architectures are summarized in Various researchers have applied this method to develop prediction models that are able to predict pipeline failure and replace time-consuming methods such as the MCS. Wen et al.  utilizedEvery ANN uses activation functions that determine the output of a neural network. Generally, they can be classified into two categories, namely linear and nonlinear activation functions. Some of the commonly used activation functions are summarized in Some researchers have utilized ANNs to develop a model for the failure prediction of pipelines by taking into account the physical, mechanical, operational, and environmental factors. This approach has shown promising results proving the robustness of ANN models when it comes to predicting the residual life of pipelines. Zangenehmadar et al.  used thiWhen following this approach to predict the failure pressure of pipelines, the issue of having a limited amount of real-life data can be overcome using the finite element method (FEM) to generate training data for the ANN model. In a study conducted by Xu et al. , the autFEM is one of the numerical methods for solving differential equations of engineering problems. The method was originally developed to solve structural mechanics problems, and it has since been extended to other branches of engineering and science, such as heat transfer, fluid dynamics, and electromagnetism. FEM is the numerical method of choice to evaluate the integrity of a pipeline as this method can be employed in widely available commercial FEM software such as ANSYS, ABAQUS, and ALTAIR HyperWorks ,27,53,54FEM is a Level 3 evaluation method for corroded pipelines according to the American Society of Mechanical Engineers . There aIn FEM, physical systems of interest in physical reality are conceptualized into mathematical models to predict responses of interest. To assess corrosion defects, FEM idealizes the pipe body and geometries of corrosion defect with internal and external load exerted on the pipe to be represented in mathematical models such as nodes. The nodes are inputted with data of physical properties, such as modulus of elasticity, and boundary conditions, such as loads and constraints. These inputs are then computed through selected numerical solutions suited for the type of FEM analysis . It is kErrors of idealization or modeling error can be reduced when input data for the finite element mathematical model represent the real conditions of the corroded pipeline accurately. As such, accurate assessment of corroded pipe through FEM will require information such as type of analysis, material properties, defect geometry, loadings, boundary conditions, and failure criterion to enable a comprehensive and accurate prediction of failure pressure using FEM.UTS).The strength of corroded pipelines is commonly assessed through structural analysis, specifically nonlinear structural analysis as opposed to linear structural analysis. In structural mechanics, the relationship between input and output is linear for a linear system as opposed to a nonlinear system . To deteThere are two types of elements commonly used to model a steel pipeline in FEM, namely shell elements and solid elements. When analyzing hollowed cylindrical structures, shell elements reduce the dimension of the problem to 2D, whereas solid elements are employed in 3D analyses. In 2013, Sadowski and Rotter explored the viability of the elements in analyzing the failure of cylindrical tubes under global bending and concluded that solid elements are more suitable for tubes as thick as r/t = 10  while shell elements are more economical when r/t ≥ 25 in terms of computational time without noticeable loss in accuracy . Shell eAging pipelines are often plagued by corrosion with irregular shapes and complex geometry. The complex geometries of corrosion defects are often idealized into simple shapes in FEM for ease of assessment. The most common idealized defect shapes are rectangular, elliptical, parabolic, and in some cases conical shapes ,62,63,64Internal corrosion is more prevalent than external corrosion in pipelines that transport hydrocarbons in pipeline corrosion failure incidents . DespiteFEM requires a material failure criterion to be defined to properly predict the failure pressure of a corroded pipeline ,70,71. TThe error of discretization is largely dependent on the model’s element discretization. There are different types of elements of various geometrical shapes for different dimensional problems and basic functions such as displacement, stress, and strain, which are necessary to compute the data of interest within acceptable error bounds. Thus, appropriate meshing at areas of interest, such as at corrosion defect regions and pipe segments, is needed to balance between evaluation accuracy and computational time consumption.FEA predictions of failure pressure are more accurate when compared to existing standards and codes ,64,74. CFEM is more accurate for the assessment of failure pressure in a corroded pipeline when compared to the conventional assessment standards ,3,27,53.The first approach is by incorporating the ANN directly into the framework of the FEM. ANN and FEM are powerful prediction tools that have proven to produce highly accurate results while consuming less calculation or computation time compared to just FEA ,27,76. RIn addition, the direct incorporation of ANN into the FEM process was further researched by Gulikers in 2018. He developed a framework that allows substructure homogenization of complex material properties through a constitutive model captured by ANN . Data geThe second approach is by using FEM to generate training data for the development of an ANN model. As mentioned in various studies, an ANN requires sufficient training to ensure the accuracy of the model. Often in reality, many data are inaccessible, and it costs a great deal to run experiments. In such cases, parametric studies can be carried out using FEM to generate a sufficient number of data that is required for the performance of the ANN model ,80,81,82The third approach is by developing an empirical equation that represents the developed ANN based on its weights and biases. This way there is no need for advanced software to be utilized. Tohidi and Sharifi in 2016 utilized this approach and furthered their research by developing an empirical solution to predict the residual ultimate strength of steel based on the ANN model that was trained. The equation that was formulated proved to be a simple yet accurate assessment method .materiaSimilarly, the failure pressure of corroded pipelines can be estimated using this approach. Most research has been done on single corrosion defects; however, only a few studies on interacting corrosion defects have been conducted. The DNV code caters for single defects subjected to internal pressure and compressive stress and interacting defects subjected to internal pressure only. In reality, interacting defects are subjected to both internal pressure and compressive stress due to the harsh surrounding environments. Besides, DNV is recommended for medium-toughness pipes and may result in an inaccurate failure pressure prediction if used for high-toughness steel pipes . This isIn 2007, Silva et al. utilized this approach to study the relationship between interacting corrosion defects and the pipe burst pressure using FEA and ANN where FEA was used to generate training data for the ANN. In their study, they concluded that the combination of both FEA and ANN to assess the structural integrity of corroded pipelines is a promising and efficient method . In 2015Lo et al.  and VijaBased on the findings, it can be said that the integration of ANN in FEA greatly improves computing time compared to using FEA alone. A conventional FEA simulation takes up to 43,000 s, while it took only 3 s using the ANN incorporated FEA in the research of Gulikers in 2018 . The genIn time-critical situations, ANN can provide results in a matter of seconds, unlike FEM which is time-consuming. Besides, by representing the ANN as an empirical equation, no advanced software or area of expertise is needed to carry out the assessment as the calculations can be carried out simply using just a spreadsheet .The summary of the literature review and its findings are given as follows:Conventional residual strength assessment methods are generally conservative in their predictions due to various simplifications and assumptions made during their development.FEM is used extensively to evaluate the structural integrity of corroded pipelines due to its ability to accurately model corrosion defects and make predictions with high accuracy. However, FEM is computationally expensive and time-consuming to carry out comprehensively.ANNs have been widely adopted in various fields for their predictive capabilities, development, and deployment speed. Their applications in assessing corroded pipelines are unlimited, especially in predicting the structural integrity of corroded pipelines.An ANN can overcome the shortcomings of FEM through the integration of both methods.The findings showed promising outcomes in terms of predicting the failure pressure of corroded pipelines in a short amount of time without compromising on the accuracy of the results by implementing an ANN as part of the assessment framework.


Normalisation Process Theory (NPT) is frequently used to inform qualitative research that aims to explain and evaluate processes that shape late-stage translation of innovations in the organisation and delivery of healthcare. A coding manual for qualitative researchers using NPT will facilitate transparent data analysis processes and will also reduce the cognitive and practical burden on researchers.(a) To simplify the theory for the user. (b) To describe the purposes, methods of development, and potential application of a coding manual that translates normalisation process theory (NPT) into an easily usable framework for qualitative analysis. (c) To present an NPT coding manual that is ready for use.Qualitative content analysis of papers and chapters that developed normalisation process theory, selection and structuring of theory constructs, and testing constructs against interview data and published empirical studies using NPT.A coding manual for NPT was developed. It consists of 12 primary NPT constructs and conforms to the Context-Mechanism-Outcome configuration of realist evaluation studies. Contexts are defined as settings in which implementation work is done, in which strategic intentions, adaptive execution, negotiating capability, and reframing organisational logics are enacted. Mechanisms are defined as the work that people do when they participate in implementation processes and include coherence-building, cognitive participation, collective action, and reflexive monitoring. Outcomes are defined as effects that make visible how things change as implementation processes proceed and include intervention mobilisation, normative restructuring, relational restructuring, and sustainment.The coding manual is ready to use and performs three important tasks. It consolidates several iterations of theory development, makes the application of NPT simpler for the user, and links NPT constructs to realist evaluation methods. The coding manual forms the core of a translational framework for implementation research and evaluation.The online version contains supplementary material available at 10.1186/s13012-022-01191-x. Contribution to the literatureNormalisation Process Theory is widely used to design complex interventions, and to understand the dynamics of implementation processes and their outcomes.Normalisation Process Theory has been developed through several iterations, and this paper consolidates these into a single, empirically grounded, translational framework for implementation and evaluation research.This paper describes the development of a Normalisation Process Theory coding manual for qualitative research and instrument design, and it presents the coding manual ready to use.The coding manual links the primary constructs of Normalisation Process Theory to the Context-Mechanism-Outcome framework of realist evaluation.Any researcher who wishes to become proficient at doing qualitative analysis must learn to code well and easily. The excellence of the research rests in large part on the excellence of the coding.Anselm Strauss [1]The analysis and interpretation of qualitative data can make an important contribution to research on implementation processes and their outcomes when such data are interpreted through the lens of implementation theory. These data may be found in documents, interview transcripts, or observational fieldnotes. In broad terms, there are two approaches to integrating qualitative methods and implementation theory. First, by explaining phenomena of interest through procedures that identify and characterise empirical regularities or deviant cases in natural language data through processes of induction . Second,what is the work that actors do to create change? How does this work get done? And, what are its effects? Because NPT has its origins in research on the implementation of complex healthcare interventions, it does not see the intervention as a thing-in-itself, but rather as an assemblage or ensemble of beliefs, behaviours, artefacts, and practices that may play out differently over time and between settings [NPT provides a set of conceptual tools that support understanding and evaluation of the adoption, implementation, and sustainment of socio-technical and organisational innovations. NPT takes as its starting point that implementation processes are formed when actors seek to translate their strategic intentions into ensembles of beliefs, behaviours, artefacts, and practices that create change in the everyday practices of others , 11. Thesettings . It is ssettings –14.Development of the coding manual was informed by the application of methods of qualitative content analysis described by Schreier . This apA generalizable NPT coding manual is of value to researchers from a range of disciplines interested in the ways that implementation processes play out. It provides a consistent set of definitions of the core constructs of the theory, shows how they relate to each other, and enables researchers doing qualitative content analysis together to work within a common frame of analysis . In the future, as software for computational hermeneutics  becomes Despite their value to researchers, the process of creating rigorous and robust coding manuals for individual studies is rarely described, and generalizable coding manuals are rare. In this paper, we start to fill this gap. We describe the purposes, methods of development, and application of a generalizable coding manual that translates NPT into a more easily usable framework for qualitative analysis.theory-building in which core constructs of NPT have been developed and their implications explored [theory-translation in which those core constructs have been clarified and refined through methodological research leading to the development of toolkits [theory-elaboration through the development of new constructs during empirical studies and systematic reviews. These explain additional aspects of implementation processes [NPT has developed over time through contact with empirical studies and evidence syntheses, and this has led to different iterations of the theory. These have been formed through publications that have served three purposes. First, there is a set of papers aimed explicitly at explored , 16, 17.toolkits , 19 and toolkits –22. Finarocesses –25.Concept identification. The result of the iterative development of NPT is a body of constructs representing the mechanisms that motivate and shape implementation processes, the outcomes of these processes, and the contexts in which their users make them workable and integrate them into practice. These core constructs of NPT were distributed over papers that developed the theory [n=149). They identified, characterised, and explained observable features of the collective action and collaborative work of implementation  conducted for an evaluation of the accelerated implementation of remote clinician-patient interaction in a tertiary orthopaedic centre during the COVID-19 pandemic. Second, KG coded transcripts of interviews  conducted for the process evaluation of the EYE-2 Trial (an engagement intervention for first episodes of psychosis employed in early intervention in the community) [mmunity) .Further disambiguation. Pilot work demonstrated that the main elements of the ‘first pass’ coding manual were workable in practice. The piloting exercise revealed that the first pass coding manual was hard to use because it was over-complex and because it micro-managed the process of interpretation. This defeated attempts at nuanced interpretation. Additional work to disambiguate constructs and eliminate overlapping or redundant ones was therefore undertaken as we worked through steps 5–8, below.Identification of context-related constructs. Within the coding manual the contexts in which implementation work takes place remained invisible, although taking context into account had been an important element of theory development and elaboration over time [both structures and processes [ver time , 11. Therocesses . To remeFurther piloting. The four constructs relating to implementation contexts were piloted ‘in use’ by CRM on a set of interview transcripts (n=36) collected in a study of professionals’ participation in the implementation of treatment escalation plans to manage care at the end of life in British hospitals [ospitals . It was Presentation. The structure of the coding manual was then presented and discussed in a series of international webinars in February–April 2021. Discussion with participants in those webinars assisted in clarifying the ways that NPT constructs fitted together and characterised actual processes and outcomes.Agreement. Once the final structure of the coding manual was laid out, all authors then read and commented on it. This led to further ruthless editing and simplification of the coding manual.Post-submission. Journal peer review is intended to improve papers for publication. In this case, it led to a clearer and more coherent presentation of the methods leading to the development of the coding manual and of the coding manual itself. An important outcome of this process was further simplification of the construct descriptors in Tables ‘Living peer review’. Between the initial submission of a manuscript to Implementation Science (2 September 2021) and the finalisation of the manuscript , the first draft of the coding manual was viewed or downloaded more than 1600 times from the preprint servers ResearchSquare.com and ResearchGate.net. This led to useful feedback from researchers who began to use the coding manual to do ‘real-world’ data analysis as soon as it became available but who did not have specific NPT expertise. As a result of this ‘living peer review’, further simplification of the descriptions of NPT constructs was undertaken by CRM.Translating a set of theoretical constructs into a theory-informed coding manual for qualitative data analysis involves a series of tasks that are, in themselves, a form of qualitative analysis. Qualitative research focuses on the identification, characterisation, and interpretation of empirical regularities or deviant cases in natural language data. The process described here developed organically and opportunistically through these different tasks, as they were conducted, and through discussion amongst authors of this paper. The work of defining key constructs of the theory, assembling these into a framework, and then transforming them into a workable coding manual, was informed by the qualitative content analysis procedures described by Schreier .ConceptContexts are events in systems unfolding over time within and between settings in which implementation work is done .Mechanisms motivate and shape the work that people do when they participate in implementation processes .Outcomes are the effects of implementation work in context—that make visible how things change as implementation processes proceed .Working through the procedures described above led to part A of the coding manual for NPT. This is presented in Table These 12 constructs form a general set of codes that can be applied to almost any textual data whether these are fieldnotes, interview transcripts, or published texts. They are all grounded in empirical research. Part A , each possess four associated secondary constructs. These secondary constructs provide further and equally empirically grounded codes where the available qualitative data support interpretation at that level of detail. Once again, each construct is named and briefly described, and each descriptor is accompanied by an example of the empirical application of the construct in already published work. However, the use of these 16 secondary constructs in coding is not mandatory, and many papers included in systematic reviews [overcome perceived problems of context. In NPT, analysis always focuses on purposive social action—the work that people do to enact evidence or innovation in practice—and for this reason, focusing attention on the constructs that characterise action is central to the interpretive task.More granular possibilities are presented in part B Table  of the c reviews –14 of NPThe purpose of developing this coding manual was to clarify and simplify NPT for the user and to make it more easily integrated and workable in research on the adoption, implementation, and use of sociotechnical and organisational innovations. In qualitative content analysis—as in other forms of qualitative analysis—proliferating constructs can easily make the business of coding ever more microscopic and can mean that it becomes less analytically rewarding. Indeed, the more parsimonious a prescheduled theoretical structure is, the more space it provides for nuanced interpretation and the development of novel categories of data and the analytic constructs that can be derived from them.In the development of the NPT coding manual described here, we sought to eliminate ambiguity and add workability from the outset. The process of selection and structuring we describe yielded a set of 12 primary NPT constructs (Table Coding is a centrally important procedure in qualitative analysis , but it mechanisms that motivate and shape implementation processes [contexts [outcomes [Linking NPT to the CMO model of realist evaluation did not happen by accident. NPT was developed through a series of iterations that were already heading in this direction. This began with empirical studies that led to rigorous analysis of the rocesses , 8. As tcontexts , 13, 16 outcomes , 25, 55.outcomes –14 reveaWe describe a set of methods likely to be useful be useful to qualitative researchers in other areas of research who wish to consider developing such manuals for other theories (for example, relational inequalities theory  or eventThis work was undertaken opportunistically and grew organically. The manual thus developed cumulatively and in an ad hoc way. Working from a structured protocol would have added greater methodological transparency and perhaps also potential for replication of the development process. Finally, researchers working from different perspectives, with different experiences of NPT, using primary empirical studies rather than theory papers, or working from a prescheduled protocol, might have produced a different coding manual.This paper describes the procedures by which the NPT coding manual for qualitative research was produced. It also presents the manual ready for use. But more than this, the process of producing the coding manual has also led to the simplification and consolidation of the theory by bringing together empirically grounded constructs derived from multiple iterations of theoretical development over two decades.Coding manuals are useful tools to support analysis in qualitative research. They reduce cognitive load and at the same time render the assumptions underpinning qualitative analysis transparent and easily shared amongst teams of researchers. The coding manual makes the application of NPT simpler for the user. This adds value to qualitative research on the adoption, implementation, and sustainment of innovations by providing a stable, workable, set of constructs that sit comfortably alongside the well-established model of realist evaluation . It alsoAdditional file 1.