dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeling an 'Assessor's Shift' in Assistance to Scientific Reasoning We come later (Section 2, Background) to the corpus of the literature that underlies our "multiverse graph for scientific reasoning". The main assumptions for the development of GRAPHYP 2 (GR2) are first sketched below to provide a perspective to our research question. Research methods are developed in Section 3. We assume that the assistance to scientific reasoning should rely on data captured from web usage traces, left in usage logs or in a restricted form, from any dataset equipped with a log record. The challenge is then to model differing uses and changing choices (assessor shifts) that the user can encounter, simulate and interpret, by visualizing and modeling the 'documentary tracks' (i. e. , the profile of the documents consulted by the users of similar queries on a research question).
Tests of data availability and first-degree analytics are given on a large scale in WorldWideScience (http://worldwidescience. org/), including experimentations on reusing log data to analyze the semantics of queries in science, with a comparative approach  [28]  and a large number of participating databases worldwide. Exploitations in large scientific infrastructure: A few examples Data capture and re-use from logs to improve services to users/researchers is at work in a growing body of very large infrastructure; we select here a few significant practices in techniques and results. The OakRidge Leadership Computing Facility, which exploits logs in petascale file systems, has been developing since 2017, profiling usage behavior trends and the sharing trends of users, and it invites other HPC centers to develop a similar approach  [29]  with new methodologies  [30]  for efficient web mining taxonomy.
Visualization of User Selections of URLs Consulted during the Search Opening the way to further studies implies visualizing users' selections of consulted URLs on any route, as illustrated by Figure  4. Each node (blue points) corresponds to a URL (a web page associated with a paper) for sessions in which multiple pages were viewed. An edge connects two nodes when the user has successively consulted the corresponding pages. Edges are represented either with blue lines (low frequency) or red lines (high frequency). A total of 4704 nodes and 4550 edges for 1532 sessions are represented here. Nodes are positioned using the Fruchterman-Reingold force-directed algorithm (Spring Layout of Network Python package). Some long routes appear, with most of them in the middle of the graph. Figure  4  offers another representation of the GRAPHYP source data of Figure  3  presents a more direct visualization of the selections made by the readers and their sea strategies, initiating further interpretations.
• Real world practices and elaboration of patterns Practices of observation and the exploitation of usage logs benefit scientific workflows such as Embase and its parallel universe, Scopus  [66] , to evaluate the degree of overlap between the "offer" and "demand" of scientific documentation through "the search carried out for a systematic review investigating validated existing track and trigger scores". It creates a new method for the 'canonicalization' of the dataset's uses and modeling at W3C with ongoing discussion on new standards (https://www. w3. org/TR/rdf-canon/#introuses/, accessed on 14 February 2023).
• Search patterns and the canonicalization of datasets Search patterns in the literature in the context of scientific research are opportunities to develop original analyses of "non factoid" answers  [69]  that aim at "investigating research gaps and research objectives that are promising to obtain insight into future research directions" and for which GRAPHYP's methodology provides a prototype. Multimodal search creates the need to improve observations of new interactions between research documentation and influence, such as from other sources  [70]. Are there modellable behaviors in the search experience? How are those behaviors related to sense-making activities on web graphs (WebGraph: https://webgraph. di. unimi. it/, accessed on 4 March 2023)? Is scientific research characterized by specific regularities?.
Web mining usage and its related analytics too often remain an unfertilized commonality of cognitive communities in research activities; on the web as well as in databases, modelized data on the use of the web remains a kind of "res nullius" of the Roman era  [72]. Modeled uses of predecessors are too often ignored, hidden or hardly defined and ill-protected. However, retrieving a relevant result on the web remains an ambiguous practice. On one hand, the progression toward the result is instantaneous and obvious, and on the other hand, the diversity of neighboring results is so rich that the search remains full of unanswered questions. As keywords provide access to a zoo of meanings within which we are compelled to express our preferences, we show in an inverted approach what are the adversarial features of the existing choices to allow everyone to benefit from the comprehensive mapping of possible results, either to find others features to reach the same place or other places that can express the same features.
The method is here related to the graph's assortativity approaches described in Wolfram Assortativity (https://reference. wolfram. com/language/ref/GraphAssortativity. html accessed on 2 August 2022). With the three above described parameters, two triplets can be shaped to represent a formal graph-based representation of paths between two limits fixed to the expression of the preferences of users. With the design of Figure  A1  hereunder, we can position between these two limits the six non-contradictory solutions that combine values of parameters that can be connected between those two limits. It shapes the following bipartite crown graph with connected nodes, representing six typical networks of nodes; those subnetworks in GRAPHYP represent the modelizable cliques of any cognitive community in our conceptual framework.
This paper presents inserexs, an open-source computer program that aims to pre-evaluate the different reflections for resonant elastic X-ray scattering (REXS) diffraction experiments. REXS is an extremely versatile technique that can provide positional and occupational information about the atoms present in a crystal. inserexs was conceived to help REXS experimentalists know beforehand which reflections to choose to determine a parameter of interest. Prior work has already proven this to be useful in the determination of atomic positions in oxide thin films. inserexs allows generalization to any given system and aims to popularize resonant diffraction as an alternative technique to improve the resolution of crystalline structures.
Here we present our application inserexs (intensity and sensitivity comparator for REXS), conceived to help REXS users decide which reflections should be explored in order to determine a certain parameter of interest. The framework was written in Python and consists of several modules that communicate between each other, as detailed later. Implemented multithreading capability allows the simultaneous operation of the several modules that compose the general program. Simulation of the spectra is performed with the ab initio software FDMNES (Buna ˘u & Joly, 2009), which uses time-dependant differential functional theory (TD-DFT) with the Hubbard correction (LDA+U) for the generation of spectra. The user interacts with inserexs Schematic of a REXS setup and experiment. Green -incident beam; blue -diffracted beam.
Feature overview 2. 1. Reflection choice: intensity and sensitivity Judging the adequacy of each reflection to determine a certain parameter is the main objective of inserexs. This 'usefulness' or adequacy will depend on two factors. First, the more intense a reflection, the better the signal-to-noise ratio for the measurement, and the better the spectrum fitting and the refinement. Therefore, the intensity of the reflection will be the first parameter to consider. For this intensity value, inserexs calculates the crystal structure factor F hkl ¼ P i f i exp½2iðhx i þ ky i þ lz i Þ and considers the intensity to be I hkl / F hkl Â F Ã hkl. This allows the program to know which reflections are allowed (non-zero intensity). For such a purpose, inserexs generates all the atomic positions (from the symmetry operations and the positional information in the.
cif file) and calculates F hkl , considering the atomic scattering factor (f i ) of each atom. By default, inserexs considers a reflection to be active when its intensity is larger than 0. 1% of the intensity of the most intense reflection. This value can be manually changed in the intensity module. For quick simulations, inserexs makes a rough approximation by considering the atomic scattering factor to be the number of electrons of the atom. However, on request (by selecting 'Include forbidden reflections' in the GUI), inserexs can precisely calculate the f i of each atom using the numerical tables of anomalous scattering factors calculated by the Cromer and Liberman method  (Sasaki, 1989). For this last method to work, the file Sasaki_anomalous (distributed alongside inserexs) must be present in the directory where inserexs is executed.
Code composition and module interaction The program is composed of three main modules which interact with the outer program FDMNES as shown in Fig. 4. The first is the central module, whose function is to connect the rest of the modules and provide a backbone to the whole application. It is also directly linked to the GUI, where the input takes place. It is further responsible for data assembly and final display. The other two modules are in charge of obtaining the intensity and sensitivity values, respectively. In the GUI, the user can load the crystal data as a. cif file. Alongside the inserexs main code, a 'cif builder' tool has been distributed for cases where a. cif file is not available. The. cif file should follow the conventions indicated in International Tables of Crystallography  (Bernstein et al.
, 2016). The code attempts to extract the different crystal parameters from the CIF, and these can be subsequently modified and updated directly from the GUI. Several. cif files have been successfully tested, each one with different writing styles and amounts of information. However, some lines will be mandatory for the correct functioning of inserexs. The complete list can be found in the user guide. All the CIF information is sent to the intensity module, which uses the crystal information to obtain the intensity of the non-null reflections. For this purpose, the program generates all the atomic positions from the. cif file's atomic positions and symmetry operations. Then, redundant positions are removed and each unique position is used to calculate the structure factor for each reflection.
The output intensity has been simplified as the square of the structure factor. The code considers a general triclinic case when angledependent calculations are needed for the atomic scattering factor. The set of intensities for each reflection can be retrieved from all these steps, although similar results are produced (and faster) using the intensities given by FDMNES. The code requires the user to choose from the GUI which parameter will vary, how much it will vary, the energy range (start, stop and step) and the edge on which the simulation will take place. The user can also choose if all the parameters will vary simultaneously or if they should vary in an unrelated manner. All this information, alongside the list of active reflections, is sent to the sensitivity module so that valid FDMNES input files are generated.
generates the simulations. A cluster radius of 5 A ˚was chosen after multiple trials (a good compromise between exact results and reasonable calculation time). The Green formalism is used for the calculations. If the chosen edge or the chosen parameter does not respect the crystal structure, an error arises at this point. If the process has been successful, the sensitivity module loads the FDMNES output data, treats them and calculates the sensitivity as previously defined. Finally, a sensitivity versus intensity plot is shown directly in the GUI. The plot is also automatically saved in the folder where the. cif file was found.
Conclusions We have shown how the inserexs code can evaluate the active reflections of a system for a REXS experiment, considering both their intensity and how sensitive they are with respect to the studied parameter. The program has been demonstrated on three completely different materials, for three different purposes and using different simulation conditions. From a user-friendly GUI interaction, the program uses a. cif file to send the crystallographic information to its several modules, from which the intensity and sensitivity values are obtained. This program will help any potential REXS user in deciding which reflections to choose before any real experiment takes place.
Funding information This work of the  Interdisciplinary Thematic Institute QMat , as part of the  ITI 2021-2028 programme  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  (grant No. ANR 10 IDEX 0002 ) and the  SFRI STRAT ' US  project (grant  Nos. ANR 20 SFRI 0012 ;  EUR QMAT ANR-17-EURE-0024 ) under the framework of the  French Investments for the Future programme.
, 2020). Increases in SOC increase subsequent crop water availability through increasing water holding capacities  (de Moraes Sá et al. , 2017; Zdruli et al. , 2017). As SOC and crop yields influence each other, but each responds individually to changing temperature, soil moisture, elevated [CO 2 ] and crop and soil management  (Lugato et al. , 2007) , one can expect that projected climate change impacts on crop yields depend on consideration of crop management affecting SOC  (Prior et al. , 2005; Marhan et al. , 2008). However, datasets to allow a more comprehensive understanding of the complex interactions between tillage, cropping system and fertilization related to greenhouse gases emissions and SOC sequestration from croplands  (Johnson et al. , 2005)  and models representations of these complex interactions are subject to considerable uncertainties  (Lutz et al.
Model description This study used a crop model solution in the SIMPLACE modeling framework (www. simplace. net), combining the Lintul5 crop growth model  (Wolf, 2012) , a modified version of the soil water balance Slimwater  (Addiscott et al. , 1986; Addiscott and Whitmore, 1991)  model, the FAO-56 dual crop coefficient procedure for calculating crop evapotranspiration  (Allen et al. , 1998) , the NPKDemandSlimNitrogen module  (Addiscott and Whitmore, 1991; Porter, 1993; Jamieson et al. , 1998) , an hourly canopy temperature module  (Webber et al. , 2016) , a heat stress module  (Gabaldón-Leal et al. , 2016)  and the soilCN module  (Corbeels et al. , 2005a). Lintul5 is a generic crop growth model which simulates crop growth under potential, water and nitrogen (N), phosphorous (P) and potassium (K) limitation.
Nitrogen Historical nitrogen data from 1982 to 2006 related to nitrogen inputs into European cropland were derived from data on nitrogen fertilizers, residues, atmospheric N deposition and manure (estimates based on animal numbers to derive manure input) from the CAPRI database for each NUTSII level  (Webber et al. , 2015). As data in CAPRI do not distinguish irrigated from rainfed production at the time of the study, a simple rule for nitrogen allocation was devised to estimate fertilizer N for simulations for both production system following the approach of  Webber et al. (2015)  and  (Zhao et al. , 2015). The rule is that the amount of nitrogen allocated to irrigated crops is set to 2 times the rate allocated to rainfed crops for any given location and the sum of nitrogen applied to irrigated and rainfed systems equals the value reported by CAPRI.
Climate data Daily climate data at 25 km resolution were derived from the Joint Research Center's (JRC) Agri4Cast database (version 1. 0) including daily minimum, and maximum air temperature, precipitation, global radiation, wind speed, actual vapor pressure, relative humidity at maximum air temperature and dew point temperature were used (http://open-research-data-zalf. ext. zalf. de/ResearchData/DK_59. html). Data were available for a baseline period  and the scenario period (2040-2065) for three RCPs: RCP2. 6, RCP4. 5 and RCP8. 5 and five GCMs: GFDL-CM3, GISS-E2-R, HadGEM2-ES, MIROC5, and MPI-ESM-MR. Only two GCMs (HadGEM2-ES and MPI-ESM-MR) were available for RCP2. 6 at the time of the study. Baseline data were driven from interpolated observed data at station and the scenarios were calculated using an enhanced delta change method that applies changes in aspects of temperature and precipitation variability in addition to changes in mean climate  (Ruane et al.
Soil data Soil data was derived from two datasets: the European Soil database from the JRC European Soil Data Portal (http://eusoils. jrc. ec. europa. eu/) and the Land Use/Cover Area frame Survey) (LUCAS,  Orgiazzi et al. , 2018). The original JRC available data, at 1 km resolution, was resampled to the 250 m resolution of the Corine Land Cover 2000 raster to enable selecting only soil values associated with agricultural land (non-irrigated arable land; permanently irrigated land; rice fields; annual crops associated with permanent crops; complex cultivation patterns; and land principally occupied by agriculture with significant areas of natural vegetation). Data were then aggregated to the 25 km grid of the climate data by selecting the median soil depths available for the root growth, total available water, bulk density, total soil organic carbon and texture (clay, silt and sand).
The selection was based on the soil class having the largest area in each 25-km unit. The LUCAS database (https://esdac. jrc. ec. europa. eu/content/luca s-2009-topsoil-data) was used to derive the top soil organic carbon (20 cm) of the reference year 2009. We first joined the LUCAS top soil organic carbon geo-referenced samples (a total of 19967) to the corresponding LUCAS land cover database and selected a subset of 8829 samples that were classified as crop land. These samples were then joined to the JRC 25 km grids defined by the climate data. Within each grid, the mean value of top soil organic carbon was determined by averaging over all LUCAS samples. For missing grid cells, we used an extrapolation approach to select the value of the neighboring grid cell as an approximation of the soil organic carbon.
Crop rotations Crop rotations were selected using historical statistical data  from the CAPRI database at NUTSII level. Data were first aggregated over years for each NUTSII region and crop, and then again aggregated to environmental zones (ENZ). The share of each crop production activity per utilized agricultural area was calculated and ordered for the crops simulated by SIMPLACE in this study. Up to six crops were selected for each ENZ based on the highest area shares. Then for each ENZ, the selected crops were combined into plausible rotations based on good agronomic practices and phytosanitary impacts of the preceding crop. The agronomic rules used to generate the rotations followed the approach of  (Reckling et al. , 2016a (Reckling et al. , , 2016b ): (i) combine selected crops to produce all possible two-crop combinations by applying sequence restrictions defined with expert knowledge on plausible crop rotations considering timing, nutrient demand and pest and disease management to validate the rotations generated with the method of (Reckling et al.
Model parameterization and testing The SIMPLACE model has been widely applied at European scale for assessing the climate change impacts  (Webber et al. , 2015 (Webber et al. , , 2018;; Zhao et al. , 2015)  and for modeling crop rotations  (Kollas et al. , 2015; Yin et al. , 2017). For phenology, observations of crop sowing, anthesis, maturity and harvest from the JRC Mars database (https://ec. europa. eu/eurostat/we b/main) were used to calibrate the crop model thermal time parameters. First phenology observations were aggregated to the level of environmental zone across Europe  (Metzger et al. , 2005). Then the phenology observations were assigned to each simulation unit (grid cell), and thermal time requirement TSUM1 (from emergence to anthesis) and TSUM2 (from anthesis to maturity) were calibrated for each simulation unit based on the approach used in  Zhao et al.
A sensitivity analysis was performed to determine the most sensitive parameters in the soilCN module using a simulation period for 400 years though repeating the same 30 years of weather data. The parameters used in the sensitivity analysis were identified based on literature concerning this and other SOC models (Table  S1 ). The fast99 method of  Saltelli et al. (1999)  was implemented in R with the sensitivity package. The results of the main effects and the interactions for each parameters are represented for the case of no fertilizer application with 100% residue retention in Fig. S4. The soilCN was parametrized using specific crop characteristics as well as soil variables such as soil sand and clay content, soil depth of each horizon, surface litter amount, litter composition, depth of mineralization, where parameter values used were taken from literature.
Simulation results aggregation and relative yield change Fully irrigated and fully rainfed simulations were conducted for each of 8057 simulation units across the EU-27 at 25 km x 25 km resolution Simulated grain yields were aggregated over each simulation period and crop rotation using the MIRCA2000 2  (Monthly Irrigated and Rainfed Crop Areas around the year 2000) dataset to allowing weighting of rainfed and irrigated simulations at the simulation unit level (Eq. (  2 )). The MIRCA2000 dataset was also used as an area weighting based on current production areas for aggregation to EZ or European level. Final yield was determined by production divided by harvest area for a given level after combining the shared rainfed and irrigated production. In each simulation unit production was determined as the yield simulated by production area, while at European level production was determined as the sum over all pixels with: Y EU = P A (1) where Y EU yield at EU level either for rainfed or irrigated using MIRCA production area, P is the total production (rainfed or irrigated) and A is the harvest area (rainfed or irrigated).
, 2015). Importantly, our SOC model relies on modeling concepts on soil organic matter (SOM) dynamics that are relatively outdated and not state of the art in the soil organic matter community. There, SOC is now conceived of and measured as particulate and mineral associated organic matter, differing in size and density, as well as turn over dynamics. Nevertheless, to the best of our knowledge, no cropping system models consider such pools and our SOC model is quite representative of others in crop models. Additionally, the study also suffers from the lack of a long term SOC dataset at European scale for model calibration and testing. Nevertheless, the model was tested with long term SOC dataset a field scale  (Seidel, 2020). A second dataset were considered for model evaluation. This study reports SOC changes from a long term experiment with nine crops (winter wheat, sugar beet, silage maize, winter rye, linseed, potato, spring barley, field pea and pea) from Brandenburg, Germany, a continental agro-ecological region with very sandy soils (Fig.