dataesr/scientific-paragraphs-categorization huggingface dataset:
The pixels classified at least once as fallow/idle cropland [code 61] (henceforth: 'fallow land') or cropland  [codes 1-60, 66-77, 204-254]  (henceforth: 'cropland') were included in the mask, and other land cover classes were excluded from the mask. The resulting mask contained more than 18. 75 million pixels, or an area of 16,877 km 2. Next, we constructed a time-series that reports in a tabular format the sequence of land-uses for all pixels contained in the total agricultural land mask. We added the FIPS State Numeric Code, and the Ecoregions Level III Numeric Code of all entries. To determine the codes, we overlaid the time-series data with the states and Level III ecoregion boundaries. The Python processing scripts are available on OSF/GitHub (https://osf. io/c7n3u/).
Data analysis We calculated three groups of indicators to analyse the spatial patterns and temporal dynamics of fallow land in relation to cropland: (i) the spatial extent of fallow land and cropland, (ii) fallowing and cropping frequencies, and (iii) post-fallow land-use types. To describe the spatial extent of fallow land and cropland, we estimated the area of fallow land and cropland in square kilometres, and the yearly percentage of fallow land and cropland at three scales: RGB level (i. e. for the entire U. S. RGB SES), state level, and ecoregion level. We also estimated the percentage of fallow land for each ecoregion within each state to determine if for ecoregions spanning multiple states, trends were different among jurisdictions. To assess the fallowing and cropping frequencies, we counted the number of times a pixel was classified as fallow land or cropland over the length of the time-series (11 years).
We mapped the frequencies and aggregated the pixel frequencies for the RGB SES. To determine the major post-fallow land-use categories, we estimated the cumulative area of each land-use following fallow over the entire time-series. We assessed their respective proportion by dividing the cumulative area of each post-fallow land-use category by the total area of fallow. The share of post-fallowing land-use was measured at the RGB SES and state levels. We used R (version 3. 5. 3) and ggplot2 package to calculate the indicators and create the plots. We calculated the frequencies and created the maps with arcpy and ESRI's ArcGIS. The Python and R scripts for calculating the indicators are available on OSF/GitHub (https://osf. io/c7n3u).
Data accuracy NASS performs an accuracy assessment of the CDL classification each year at the state level. To do so, the CDL maps are compared against independent datasets from the CLU for all cultivated land-cover classes and against the NLCD for non-agricultural land-cover classes (USDA NASS, 2018a). Annual error matrices that report the number of times a particular CDL land-cover class is consistently mapped or not, along with several accuracy statistics, are released and published on the NASS website (USDA NASS, 2018b). The CLU, which is one of the most complete datasets of U. S. agricultural land-cover, provides a robust ground-truth reference dataset for land-cover classification validation. However, it is not publicly available  (Johnson, 2013). Drawing on the annual error matrices and based on the equations proposed by  Olofsson et al.
Limitations and next steps Our approach includes several limitations. We chose the CDL dataset, since this is the longest timeseries and the best crop-specific land cover satellite-derived dataset available at a 30 m resolution for the contiguous U. S. (Johnson, 2013; Wright & Wimberly, 2013). As a result, its use has been growing steadily, for example, to track land-use and land-cover change  (Lark et al. , 2015; Wright et al. , 2017; Wright & Wimberly, 2013) , to derive maps of tilled cropland  (Johnson, 2013) , to assess the impact of landscape-scale agricultural diversity on U. S. crop production  (Burchfield et al. , 2019) , and to quantify the determinants of land-cover change  (Stoebner & Lant, 2014). However, tracking fallow land area dynamics with the CDL poses several challenges.
As recommended by  Lark et al. (2017) , we characterized the bias but, instead of limiting our study to a bi-temporal change analysis, we quantified fallow dynamics over the entire time-series for comparisons across states and years. Our results have shown that almost 75% of the area fallowed over the time-period was in fallow once or twice, but this could also be the result of CDL misclassifications. 36% of the area in fallow was followed by shrubland, grassland/pasture, barren, or developed/open space land (Table  2 ). Instead of an actual change in land cover, this could be the effect of classifier confusion, which is notoriously hard to differentiate  (Johnson, 2013; Lark et al. , 2017). However, to date, the CDL is the best and longest crop-specific land-cover dataset available for the U.
Purpose: Online and offline software products can estimate the maximum skin dose (MSD) delivered to the patient during interventional cardiology procedures. The capabilities and accuracy of several skin dose mapping (SDM) software products were assessed on X-ray systems from the main manufacturers following a common protocol. Methods: Skin dose was measured on four X-ray systems following a protocol composed of nine fundamental irradiation set-ups and three set-ups simulating short, clinical procedures. Dosimeters/multimeters with semiconductor-based detectors, radiochromic films and thermoluminescent dosimeters were used. Results were compared with up to eight of 10 SDM products, depending on their compatibility.
The dosimetric capabilities and accuracy of the SDM products could strongly differ depending on the irradiation settings and the interventional X-ray system tested. Those studies, however, were performed following different measurement protocols and/or for different patients, which makes comparison difficult. The protocols were also not intended to be used in clinical practice and could be challenging to perform on a regular basis. To date, no acceptance testing nor quality control (QC) protocols have been published for such products. The European-funded project VERIDIC (Validation and Estimation of Radiation skIn Dose in Interventional Cardiology) aimed to contribute to the harmonisation of radiation dose structured reports (RDSRs) and the validation of SDM software products in interventional cardiology.
Three work packages were created to (i) review the existing SDM products and identify the need for RDSR standardisation, (ii) contribute to the development of a protocol for testing the accuracy and the performance of the SDM software products and (iii) investigate the determinants of high skin doses. Links to the project results are available on the project web page  [20]. This article reports the results of the second work package of the VERIDIC project. In particular, the results of the measurements performed on four interventional X-ray systems following a common protocol are presented and a comparison with the calculated maximum skin dose (MSD) for up to 10 SDM products is made. Protocol requirements for developing acceptance testing and QC protocols for SDM products useable in clinical practice, are discussed and a summary of the limitations are given.
Skin dose mapping software tools Skin dose estimates were performed with Not all SDM products could be combined with the four interventional X-ray systems used for the measurements, mainly due to compatibility issues. The interventional X-ray systems tested with each software product is reported in Table  3. CAATSDOSE, em. dose and OpenSkin were specially modified for the present project in order to allow skin dose estimates for Philips and Canon interventional Xray systems. DTS and DoseMap were the only products which could display both the MSD and the dose map online. All software but CareMonitor produced a map of the skin dose distribution; however the maps produced by DoseMap could not be retrieved due to a faulty manipulation at the time.
Deviation of the measured MSD For irradiation set-ups 1 to 9 and during the simulated procedures, the ratio of the calculated and the measured MSD values was calculated as: 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟(%) = � 𝐷𝐷 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝐾𝐾 𝑐𝑐,𝑒𝑒 × 𝑓𝑓 𝑐𝑐𝑎𝑎𝑎𝑎,𝑤𝑤𝑐𝑐𝑤𝑤𝑒𝑒𝑎𝑎 � × 100 % Equation 1 And the deviation between the calculated and the measured MSD values was calculated as: 𝑑𝑑𝑑𝑑𝑑𝑑𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑑𝑑(%) = (100% -𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟(%)) Equation 2 Where Dcalc is the MSD calculated by the SDM software, Ka,e is the entrance surface air kerma as measured by the dosimeter and fair,water is the ratio of mass energy-absorption coefficients. A single value of 1. 06 was used as it limits the uncertainty for the energies encountered in IC as reported by Benmakhlouf  [29]. No acceptability criteria currently exist for SDM accuracy. A 40% deviation range was used for the purpose of the result analysis.
Results from CAATSDOSE and OpenSkin (all four systems); and CareMonitor, DoseMap and DTS (on Siemens, GE and Canon systems only, respectively) were less consistent and could exceed 40% even for thicknesses between 20 and 25 cm, which would cover numerous IC patients. Some SDM products also provided MSD estimates within ±20% for all phantom thicknesses on a specific system (e. g. DOSE, em. Dose, RDM, DWSDM flat and ICRP and SkinCare on the GE system). The coefficients of variation (COVs) of the MSD estimates for a specific phantom thickness was relatively small for Philips (16% to 19%) and GE (12% to 21%), which indicates that most software provided comparable estimates. COV ranges could be higher for the Siemens system (16% to 25%), mainly because of some outlying values for irradiations of small PMMA thicknesses.
Rando Alderson phantom (set-ups 6 to 9) The ratio of the calculated and the measured MSD values for the fundamental irradiation set-ups on the RA phantom (set-ups 6 to 9) are reported in Figure  6  to Figure  9  for Canon, GE, Philips and Siemens systems, respectively. Only three SDM products provided MSD estimates within 40% for all RA phantom irradiations on the tested systems: SkinCare (on all four interventional X-ray systems), DOSE (GE, Philips and Siemens) and DoseMap (on the GE system). Three software products (em. dose, OpenSkin and DWSDM flat) could not provide MSD estimates for lateral irradiations. Excluding the lateral irradiation (event 9), all SDM products with the exception of CAATSDOSE on the Philips system provided MSD estimates within 40% on all four tested X-ray systems.
Maximum skin dose accuracy The ratio of the calculated and the measured MSD values for three simulated procedures on the RA phantom are reported in Figure  6  to Figure  9  for Canon, GE, Philips and Siemens systems, respectively. Five SDM products provided MSD estimates within ±40% for the three simulated procedures on the tested systems: OpenSkin (on all four interventional X-ray systems), SDM flat and ICRP (GE, Philips and Siemens), RDM (on GE and Siemens) and DoseMap (on the GE system). Results of the remaining software products showed more variability: em. Dose and SkinCare were within ±40% for all four systems but the Philips one; CAATSDOSE was within ±40% for the Canon and GE systems only; and DOSE exceeded ±40% for at least one simulated procedure on each tested system (GE, Philips and Siemens).
The different patient representations, colour scales and resolutions made comparison challenging. Limited modifications (such as cropping or reorientation) of the dose maps as created by SDM software were therefore performed in order to facilitate a qualitative comparison. From a qualitative, visual comparison, it appears that all maps reproduced the dimensions and the relative position of the region of the MSD on the GE and Siemens systems accurately. An exception was CAATSDOSE and OpenSkin's first procedure maps, which show an excessive distance between the two central high-dose regions as measured with the films. The situation was more contrasted for the procedures performed on the Philips systems. The estimated shape and size did not represent the measurements, whereas the relative position of the MSD region was correct (except for SkinCare which depicted the MSD at the intersection of two fields not overlapping in the measured map).
PMMA slab phantom (set-ups 1 to 5) On the Siemens system, there was a clear improvement in the MSD estimates from all SDM products when the PMMA phantom thickness increased. This may be due to an overestimation of the phantom thickness and thus the backscatter factors (BSFs) for the thinner phantom thickness. This assumption could be further strengthened by the transmission on the Siemens system varying considerably for lower kVp/HVL while most SDM products use a single table transmission value for all beam qualities. CareMonitor estimates were within 40% of the measured doses for PMMA thicknesses higher than 20 cm, although that software product only provides an estimate of the maximum cumulative Ka,r. The rather good agreement between the maximum cumulative Ka,r and the MSD for the thicker PMMA phantoms might be due, in part, to the fact that the table/mattress transmission and the BSFs increase with the phantom thickness (and the beam quality).
Ideally, developers should implement methods to accurately determine the patient contour; in the meanwhile, implementing PMMA slab phantom models in their software tools, would be an efficient solution. In addition, to the authors' knowledge, DTS is calibrated only with a 20 cm PMMA phantom on site as presented in  [8] , which can further explain some discrepancies. When considering the MSD estimates of a specific SDM product on multiple interventional X-ray systems, no clear trend between the phantom thickness and the MSD accuracy could be observed. For instance, the accuracy of DWSDM (flat and ICRP) estimates seemed to be stable irrespective of the phantom thickness on the Philips system, while accuracy increased as the phantom thickness increased on the Siemens system; and inversely on the GE system.
Rando Alderson phantom (set-ups 6 to 9) Results of the PA irradiations of the RA phantom (set-up 10) were expected to be identical to the irradiations of PMMA thicknesses of about 20 and 25 cm (set-ups 4 to 5). This was, in general, not observed. This could be in part explained by a combination of system-specific algorithms and difference in material compositions and equivalent thicknesses, resulting in the selection of a different tube voltage and filtration. Indeed, PMMA is not exactly tissue equivalent and the RA phantom also contain lungs. In addition, the probe of the QC dosimeter was also in the X-ray field for PMMA irradiations but not for RA irradiations. Moreover, some SDM software use height and weight as input for selecting the patient model and backscatter factors. The implementation of PMMA slab and RA phantoms would address that issue.
Three software products (em. dose, OpenSkin and DWSDM flat) could not provide MSD estimates for lateral irradiations because a flat phantom was only used. Among the remaining software products, results were variable for lateral irradiations. For instance, CAATSDOSE and DOSE estimates were in agreement with the measurements for lateral irradiations on all tested systems; RDM estimates were in agreement for the GE system but not for the Siemens; whereas DWSDM ICRP and DTS provided estimates with low accuracy, although good accuracy was observed for all other RA phantom irradiations. This variability is not surprising because there is no information from the modality or from the RDSR about the X-ray-source-to-skin distance (i. e. the distance between the patient entrance surface and the X-ray source).
This is not surprising since the RDSRs are not completely standardised among manufacturers. Besides, irradiation settings were not completely identical across the tested systems. In particular, X-ray systems from different manufacturers implement different algorithms for the selection of the tube voltage and beam filtration, and those parameters directly affect the accuracy of the SDM estimates. For the same reasons, the performances of a specific SDM product combined with a single X-ray system were quite consistent. Anthropomorphic phantoms offer the most realistic patient representation. One (DWSDM ICRP) of the three tested software tools implementing anthropomorphic phantoms had one phantom model available (ICRP phantom), irrespective of the patient morphology. The two remaining software tools (DTS and SkinCare), used an extended library of anthropomorphic phantoms covering different weight and morphologies.
Comparison with literature In Table  5 , the benchmarking results as available in the literature for 7 SDM products are summarised and compared with the present study for similar configurations (phantom and systems). To the authors' knowledge, no peer-reviewed article presents studies on the experimental accuracy of CAATSDOSE, CareMonitor, DOSE and OpenSkin. SDM products were validated on one X-ray system; only em. dose and RDM were validated against two systems. SDM calculations on a specific system as observed in the present study usually showed a tendency to either underestimate or overestimate the measurements, but not both. This was not the case in the literature data with the exception of two studies on DoseMap  [10]  and DWSDM  [18]. The absolute maximum deviation presented for measurements on phantoms were of the same order of magnitude as those presented in the literature.
To convert the air kerma at the phantom entrance surface to water kerma (or kerma in any other medium), the energy fluence weighted ratio of mass energyabsorption coefficients is required. For beam qualities encountered in interventional cardiology, the ratio can vary between 1. 02 and 1. 12  [29]. Using a fixed value (1. 06) can lead to a maximum error of 5 %  [29]. This is also the value implemented in most SDM software products  [12]  A PMMA slab phantom was used because this is frequently used for QC measurements and thus readily available to most medical physicists. This phantom is not exactly water-equivalent. As showed by Benmakhlouf et al  [33] , BSFs for PMMA are between 3% to 10% higher than in the case of a water phantom. Measurements of the MSD using a PMMA phantom can thus overestimate calculations using BSFs determined for water.
(ii) The Ka,r accuracy and the table transmission factor as measured on the systems were used for the calculations using offline SDM products if those parameters could be readily input. Default values were used as defined in the SDM products at the time of the calculations for parameters not available in the RDSRs. This might put those SDM products which allow additional adaptation of the calculation settings to a specific X-ray system, at a disadvantage in the comparison. The dose maps were also produced with default values although some SDM products have adaptable settings for improved visualisation. It is worth noting that online software products such as DTS and DoseMap contain transmission (and backscatter) models based on multiple measurements  [8, 10] , but the accuracy of those models was not specifically evaluated.
Some software products, however, missed the MSD region when situated at the intersection of multiple fields, which is of radiation protection concern. The main limitations of the SDM software products are currently the level of data available in the RDSRs, the lack of harmonisation among manufacturers, the difficulty to select specific examination parameters and -QC -phantom models, and the need for accurate techniques for determining the patient body contour and position. Due to these limitations, it is very challenging to establish a common protocol for QC and acceptance testing. The present protocol could be seen as a basis for an interim solution while issues are addressed by the manufacturers.
Acknowledgment: The authors acknowledge  Niki Fitousi  (DOSE),  Claire Van Ngoc Ti  (em. dose), Romain Binot (RDM) ,  Claire Steinville  and  Alain Christmann  (SDM) and  Marko Krajinović  ( SkinCare ) for providing dose calculations. They also wish to acknowledge  Marcel Loepfe  for his help during the measurements on the  Canon Infinix-CF  and  Lionel Desponds  and  Jonathan Cole  for providing information relative to DoseMap and OpenSkin software, respectively.
However, the achievement of these goals could be hindered by the lack of global knowledge on soil erosion model usage. Improving such knowledge would help pave the way for more structured modelling and allow the further identification of needs to validate, measure, monitor, and map soil-erosion processes. In this study, we systematically reviewed soil-erosion modelling applications worldwide and performed a statistical analysis with the aim of addressing identified knowledge gaps and facilitating information acquisition for the new country-based UN global soil erosion assessment. The subsequent database presents the current state of knowledge on soil-erosion modelling applications worldwide. We aimed to create and share a comprehensive and unprecedented database on soil erosion applications worldwide with an open science participatory approach.
Sixty-six scientists from 25 countries representing all continents (except Antarctica) have contributed their findings, systematically reviewed all available peer-reviewed literature, and merged their knowledge. The database is available in Appendix A of this article. In our study, we provide an evaluation of (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. This approach provides insights into the worldwide state-of-the-art in soilerosion model applications and allows a synthesis of information on which processes, models, and regions have received the most evaluative attention and which require increased focus in the future.
Data collection and GASEMT database In this study, we report the results of an in-depth review of scientific peer-reviewed literature on soil-erosion modelling published in international journals between the 1st of January 1994 and the 31st of December 2017 and present in Elsevier's Scopus bibliographic database. We used the following criteria to identify articles potentially relevant for our statistical analysis: keywords soil erosion and model or name of the model (Box 1) in the title, abstract, or the keywords of the Scopus indexed articles. All articles matching the selected keywords have been downloaded and reviewed by one of the 67 soil erosion experts involved in the study. The review phase started in early 2018 and followed a participatory approach open to the entire scientific community, without any restrictions.
The authors are composed of scientists who responded to an open call for expression of interest published on ResearchGate and advertised through mailing lists and word-of-mouth. Within the first data collection phase, all authors paid close attention to the following criteria: (i) verifying the relevance of the articles with respect to the objective of the review study, (ii) recording the entries' information (hereinafter also referred to as records), and (iii) extract all information listed in Table  1  for each relevant article. As a second quality control phase, P. Borrelli randomly inspected about 5% of the articles reviewed by the authors and verified whether the gathered information was complete. P. Borrelli reviewed the database to identify and rectify the most evident inconsistencies, misclassifications, and typos.