dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods The software analytics used in the development of PPMaP was founded on the following four principles: i) local, regional and global relevance, ii) representativeness of the major agro-ecological zones within the region of interest, iii) dependence on high attribute and smallest datasets, and iv) adequate validation of the results with field trial results. VP commences at emergence, goes through the nth leaf, and ends with tasseling and flower  [15]. The RP starts at flowering and is comprised of silking, blister, milk, dough, and dent stages, and stops at physiological maturity, which is reached when all kernels have maximum dry weight  [31].
PPMaP input datasets Input data into PPMaP for modeling the developmental rate of a plant are the duration of the individual phase of phenology. The data are collected from multi-locations characterized by a large temperature gradient to estimate how the duration of each phenological phase responds to diverse growing conditions. Main observations to be recorded include the sowing date, days to emergence, days to flowering, and days to physiological maturity. Days to emergence can be recorded by counting the number of maize plants daily while the emergence date is w hen 50% of the plants in the plot have emerged above the soil surface. A few days after the emergence of all plants, many plants can be randomly selected and tagged for use to record the duration of the VP and RP. The flowering date is recorded when 50% of tagged plants produced male or female flowers.
Temperature datasets The software uses two distinct sets of temperature. The first set of temperatures is directly obtained from the weather stations within the vicinity of each field experiment. Minimum and maximum temperature for the trial period are required. The information is processed to yield the average seasonal temperature for the site during crop growth. It is associated with the duration of each plant phase when loading into the software (Figure. 1). The second type of dataset needed by PPMaP for landscape mapping is the minimum and maximum weekly/monthly temperature organized in raster files with a. flt file format. The data can be obtained from any climate-based repositories and databases. To demonstrate the functionalities of the tool applicable to tropical maize, temperature data were extracted from the Worldclim website (www.
PPMaP modeling component The modeling component is comprised of 82 factor-process based development rate functions obtained from literature; which have been applied in agricultural production, either in the context of insect or crop phenology modeling  [13, 20, 32]. A plant developmental rate model predicts the average proportion of development at a certain temperature or the fraction of development completed per unit time. These fractions are then accumulated under field conditions and are treated as independent variables during the factor -based modeling formulation. Model expressions in the PPMaP database are classified by the form of relationship and structure (linear, logistic, exponen tial, sigmoid, logarithmic, polynomial, and square root expressions). Modeling with PPMaP starts with data standardization by dividing the development period of the individual phase by the mean development period of each field experimental site average temperature.
This allows data obtained from multiple location trials with similar average temperature values to be merged for unique scrutiny. Because the direct selection of an appropriate mathematical expression to represent the development phase of a crop is challenging  [13, 20, 33] , PPMaP software model component uses its in-built statistical criteria to compare, select and display equations with the respective parameters and graph. Parameter values are estimated by fitting model equations to the field data. The Levenberg-Marquardt (LM) algorithm  [34]  is implemented in the software for the estimation of the model parameters. This algorithm combines both the 'steepest descent' and the Gauss-Newton method to appraise equations. It works iteratively to find the minimum of a function expressed as the sum of squares between the nonlinear model output and the observed datasets  [34].
The PPMaP software offers an interactive process, in which initial parameter values for the selected model are altered; followed by the matching of model output to observed data. After the initialization of the models, the LM algorithm is launched and the goodness of fit procedure is used to find the parameters of the model. The model parameter comparison is done by the coefficient of determination adjusted r-squared (R 2 _Adj)  [13, 20] ; while the Akaike's Information Criterion (AIC) and the Model Selection Criterion (MSC)  [20]  are used to choose the model that best fits the input datasets (Figure.
PPMaP mapping component A shapefile representing the boundaries of the region of interest is divided into grids and loaded into the software. The model obtained during the modeling step is run at each grid of the raster file. The gridded temperature data is also loaded and with an in-built function, the values are extracted from the database simultaneously and then arranged in a matrix format using longitude as a column and latitude as a row. A point object picks the model representing the plant development rate for a specific phase, and then sequentially replaces the variable temperature in each grid by the value obtained from the database. After computation in each grid, a new matrix is generated with the values of the development rate in the respective grid of the raster file. These values are then inversed (1/development rate) to estimate the number of days used to complete each phase of plant development.
PPMaP software implementation The following two programming methods were encompassed and combined to implement the PPMaP software: (i) a procedural approach that employed the R  [36]  constituent for model fitting and estimation of statistical criteria  [37] , and (ii) Java object-oriented programming language was used to interconnect the software components and develop the graphic user interface (GUI). This was developed on the Eclipse platform  [38]. The R constituent of the PPMaP comprises of R-scripts used to code mathematical expressions (models), processes for parameter estimation and model selection, and the mapping environment. Many R packages such as 'minpack. lm' [39] , 'MASS'  [40] ; 'maptools'  [41] , and 'maps'  [42]  are employed to handle nonlinear regression analysis and model fitting and to carry out spatial operations leading to the generation of maps.
The Java object-oriented programming allows the development of a rapid and reliable end-user interface with objects, subclasses of image, color, and font integrated into the Eclipse workbench. The 'Rserve' is a tool that is used to evaluate R codes and ensure their integration into the Java application  [43]. The 'Rserve' was applied within the PPMaP software to ensure two-way communication between the R constituent and the Java object-oriented codes. When a request is made, 'Rserve' unlocks the link to collect details of the request, and then creates another link with the R constituent to transfer information for execution and sends back the result to the GUI for display. The GUI offers a direct connection between the user and PPMaP software system. Numerous utilities are developed to bring PPMaP functionalities together in a stop screen layout to make it operate intuitively.
PPMaP model validation and performance at scale PPMaP software considers the validation as the level to which a selected model rightly predicts the duration of an individual phase of the plant when compared to experimental data. To carry out this procedure, independent data acquired from various field trial sites that were not used during model development and calibrations are recommended for use. The temperature at point location is replaced in the expression of the inverse of development rate to estimate the duration of the individual phase of the plant, and the result is compared with actual recorded data from the field trial. A well-validated model is used to measure the performance at scale. Figure. 8 shows the phenology maps of two maize varieties (MV -1 and MV-2) that are grown both in Ethiopia and Nigeria respectively.
The PPMaP software presented here is a unique computer -aided tool capable of analyzing multi-location field trial information of plants to yield complete phenology models. PPMaP encompasses a full library of mathematical expressions and statistical criteria that guide users to discovery and to select the best model to describe the temperature dependence of the development for each phase of the plant. The starting point in developing plant phenology models with PPMaP is data standardization and grouping according to stage and site-specific characterized by the average temperature in which the trials were conducted. Fitting the developmental r ate curves directly to the reciprocal of developmental duration follows this step. To account for variability among plants, PPMaP applies the distributional concept demonstrated in  [44, 45] , which assumes that variation in developmental rates at constant temperature is the direct result of variability in the concentration of rate-controlling enzymes while the developmental rate variance is proportional to the mean developmental rate.
An important attribute of PPMaP is the relative ease and speed with which the overall process of producing the model and projecting at scale to yield the phenology map of the crop is done. The software environment makes programming and coding unnecessary, and hence allows the estimates of the duration of plant phases effortlessly available to researchers. PPMaP has a graphical user interface and a collection of appropriate models for representing different processes that determine the plant life cycle. Selection is based on analytic and adequate evaluation criteria. Fitting mathematical expressions using either maximum likelihood estimation (MLE) or least -squares estimation (LSE) and treatment medians is difficult and sometimes often leads to over parameterization and convergence  [20]. Therefore, PPMaP offers a solution to the problem whereby a user may be requested to increase the number of data points or use the in -built software features that allow the use of censored data to facilitate the analysis.
Thus, PPMaP uses the same concept and has a mechanism for using independent datasets (not used for model development and calibration) obtained from diverse field trials and sites to make predictions and compare results. In this context, validation is an important instrument for scaling life cycle events of plants over a wide spatial and temporal range and for examining the crop reactions to changing or novel climatic conditions, and it provides some level of understanding and adaptation in different agro-ecological zones. Taking crop research to scale requires a change in the way studies are conducted as well as the extent to which essential advice to farmers can be provided. Two types of frameworks exist the topdown approach consisting of analyzing grid cells to cover a wide area and then downscaling the results, and the bottom-up approach in which several sites representing major crop production areas are selected for analysis followed by a spatial upscale  [47].
Consequently, PPMaP software supports the concept of researching to the appropriate scale by using the bottom-up approach. Inputting phenological data of plants to carry out analysis with geospatial representation is thus a critical step to tailoring solutions at the landscape level. Considering the diversity of agro-ecological zones where crops are cultivated, we argue that it is unproductive to conduct research studies on plant phenology without providing means of upscaling results to landscape-level while still safeguarding field relevance. Herein, we presented software embedded with a method that allows appropriate scaling to larger spatial domain research findings for decision-making. Global warming might be affecting the phenology of plants in their respective natural environments. Tools based on hyperspectral imaging exist for differentiating crop phenology and seed varieties  [10, 24].
PPMaP is a tool that could help provide location specific and near accurate length of crop growth duration of different varieties. Consequently, we propose to compact the phenology maps of different maize varieties produced by PPMaP, add features such as the name, type, color, potential yield, maturity class, ecology, resistance, and tolerance level to pests/diseases for individual varieties to develop applications. This software can be used to enhan ce the capacity of extension and agribusiness service providers (agro-dealers) in providing recommendations at various scales especially in countries where the landscape varies considerably in altitude such as Ethiopia. When a new variety is developed, the norm is to conduct multi-environmental trials to evaluate the performance of the variety before release.
Conclusions This study has demonstrated that PPMaP is a tool that has vast potential to provide a locationspecific and near accurate length of crop growth duration of different varieties by supporting the concept of researching to the appropriate scale. It is an object-oriented reproducible and extensible framework for phenology mapping at scale. Besides, PPMaP provides an easy-to-use comprehensive framework to perform the entire modeling process without the extensive nee for programing skills hence user friendly to potential users of any computer skills level. The software is thus designed to enable users to extend it and share the new data, methods , or procedures to reproduce them by other users.
Plant phenology datasets The cycle of a plant is split into different development stages or phases. Maize (crop used as to test the software) growth is divided into the vegetative (VP) and the reproductive (RP) phases. The Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 24 September 2020 doi:10. 20944/preprints202009. 0563. v1.
2. 1. 1 11 Plant phenology datasets The cycle of a plant is split into different development stages or phases. Maize (crop used as to test the software) growth is divided into the vegetative (VP) and the reproductive (RP) phases. The Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 24 September 2020 doi:10. 20944/preprints202009. 0563. v1.
Vehicle simulations Details of the modelling approach, vehicle simulation techniques, and optimization strategies were recently reported by Won et al. [37]. Briefly, the simulation tool was developed using MATLAB® and Simulink®, based on test results obtained from GCI engine experiments. The vehicle that was considered in this assessment is a typical medium-sized European C-segment passenger car that is equipped with a GCI engine platform, and it is capable of achieving 95 gCO2/km, consistent with the EU requirements for 2021. A GCI engine combines the advantages of gasoline spark-ignition and diesel compression-ignition engine characteristics. Like a diesel compression-ignition engine, the compression ratio is high, it delivers lower pumping losses by eliminating intake throttling, and the mixture is lean for improved ratio of specific heats leading to better thermodynamic efficiency  [38, 39].
In this study we estimated that the Lithium-ion battery has a capacity of about 5 kWh -this lies between a 2019 Toyota Prius Prime (8. 8 kWh)  [49]  and a 2010 Ford Fusion HEV (1. 5 kWh)  [50]. The hybrid architectures above affects the size of the battery. The battery system has to be designed to deliver sufficient traction power at any given moment, and at the same time it has to have adequate storage capacity to avoid power failure as a result of deep charge depletion. For the purpose of this study, the battery pack simulation was performed using the generic battery model that is available in the MATLAB® and Simulink® libraries based on the nominal voltage (V), rated capacity (Ah) and initial stateof-charge (% SOC). More details can be found in  [37].
Fig. 2  locates the case study sites and Table  1  compares their characteristics. Farm and household specific data were collected using the survey tool IMPACTlite  (Rufino et al. , 2013) , in a semi-structured interview format in October and November 2014. For farm mapping and calculation of field areas, GPS readings were taken of field boundaries. To complement food consumption data collected using IMPACTlite, on two occasions per site, qualitative 24-h food intake recalls were applied with women of reproductive age responsible for the household cooking of foods from ten pre-defined food groups  (Kennedy et al. , 2010). The IMPACTlite survey tool differentiated foods obtained from on and off farm (e. g. market) production. Structured surveys were performed in both countries, with the same respondents as the 24-h food intake recalls, to determine the frequency at which food items (Tables  S1  and S2 ) were consumed by the household over the course of a year.
Acknowledgements We would like to thank the following people and organizations;  Prof. Dr. Mary Abukutsa Onyango  ( JKIU ) and  Dr. Pham Hoi  ( CARES )  for supplying data for intervention crops in Kenya and Vietnam respectively,  Wesley Kidiavai  and  Salano Medgeclay in Kenya , and the  FAVRI institute ,  Wim Paas ,  Son Nuygen  and  Lan Huong in Vietnam , for enumeration, facilitation, translation and logistical help during data collection, and most appreciatively, the  Kenyan and Vietnamese  farmers for their time and patience. The authors declared that they have no conflict of interest. Regarding funding, we would like to thank the strategic funds of  Wageningen University & Research  under the program 'Global One Health' and the  CGIAR Research programs of Integrated Systems for the Humid Tropics (Humidtropics) ,  Agriculture for Nutrition and Health  ( A4NH ) and  Roots, Tubers and Bananas (RTB)  and all donors who supported this research through their contributions to the  CGIAR Fund.
I. INTRODUCTION Software is a fundamental pillar of modern scientific research, across all fields and disciplines, and the actual knowledge embedded in software is contained in software source code which is, as written in the GPL license, "the preferred form [of a program] for making modifications to it [as a developer]" and "provides a view into the mind of the designer"  [18]. With the rise of Free/Open Source Software, which requires and fosters source code accessibility, access has been provided to an enormous amount of software source code that can be massively reused. Similar principles are now permeating the Open Science movement, in particular after the attention drawn to it by the crisis in scientific reproducibility  [20] ,  [12]. All this has recently motivated the need of properly referencing and crediting software in scholarly works  [13] ,  [19] ,  [9].
In this context, we provide a contribution to the ongoing efforts to develop proper guidelines and recommendations, building upon the internal experience of Inria, the French research institute for digital sciences (http://www. inria. fr). Born in 1967, more 50 years ago, Inria has grown to directly employ 2,400 people, and its 190 project-teams involve more than 3,000 scientists working towards meeting the challenges of computer science and applied mathematics, often at the interface with other disciplines. Software lies at the very heart of the Institute's activity, and it is present in all its diversity, ranging from very long term large systems (e. g. , the award winning Coq proof assistant, to the CompCert certified compiler, through the CGAL Computational Geometry Algorithms Library to name only a few of most the well-known ones), to medium sized projects and small but sophisticated codes implementing advanced algorithms.
II. SURVEY OF PREVIOUS WORK The astrophysics community is one of the oldest ones having attempted to systematically describe the software developed and use in their research work. The Astrophysics Source Code Library was started in 1999. Over the years it has established a curation process that enables the production of quality metadata for research software. These metadata can be used for citation purposes, and they are widely used in the astrophysics field  [2]. Around 2010, interest in software arose in a variety of domains: a few computer science conferences started an artefact evaluation process (see www. artifacteval. org) which has spread to many top venues in computer science. This led to the badging system that ACM promotes for articles presenting or using research software  [4]  and to the cloud-based software hosting solution used and put forward by IEEE and Taylor & Francis for their journals (Code Ocean).
Nonetheless, we consider that a lot more work is needed before we can consider this problem settled: the actual recommendations that can be found on how to make software citable and referenceable, and how to give credit to its authors, fall quite short of what is needed for an object as complex as software. For example, in most of the guidelines we have seen, making software referenceable for reproducibility (where the precise version of the software needs to be indicated), or citable for credit (to authors or institutions), seems to boil down to simply finding a way to attach a DOI to it, typically by depositing a copy of the source code in repositories like Zenodo or Figshare. This simple approach, inspired by common practices for research data, is not appropriate for software. When our goal is giving credit to authors, attaching an identifier to metadata is the easy part, and any system of digital identifiers, be it DOI, Ark or Handles, will do.
Structure A software project can be organized either as a monolithic program (e. g. , the Netlib BLAS libraries), or as a composite assembly of modules (e. g. , the Clang compiler In these contexts the roles of a single contributor can be plural, with contributions spanning variable time extents. Authorship is even more complicated when developers resort to pseudonimity, i. e. , disguised identity in order to not disclose their legal identities. For all these reasons, evaluating the real contributions to a significant piece of software is a very difficult problem: in our experience at Inria, automated tools may help in this task, but are by far insufficient, and it is essential to have humans in the loop.
Authority Beyond good practices, most quality or certified software development projects define management processes and authority rules. Authorities are entitled to make decisions, give orders, control processes, enforce rules, and report. They can be institutions, organizations, communities, or sometimes a single person (e. g. , Guido van Rossum for Python). Some projects set up an editorial board, similar in spirit to scientific journals, with reviewers, managers and well-defined procedures (See CGAL's Open Source Project Rules and Procedures at https://www. cgal. org/project_rules. html). Each new contribution must be submitted for review and approval before being integrated. Some decisions can be taken top-down while others are bottom-up. In some cases, a shared governance is implemented.
This organization can be somehow compared to the Linux kernel development organization where Linus Torvalds integrates contributions but delegates the responsibility of software quality evaluation to a few trusted colleagues. Another important aspect is the traceability of who did what during the software project. In its simplest form, the number of lines or code or commit logs are used for tracing contributions and changes, but more advanced means such as repository mining-based metrics  [17] , bug-related metrics, or peer evaluation are common.