dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimating Effective Migration Surfaces (EEMS) The software EEMS (Estimated Effective Migration Surfaces)  [44]  was used to estimate effective migration patterns between samples for 2 different sets of individuals. For the first set, we used the total dataset of 53 samples and selected a polygon accounting for the known species distribution using a grid of 600 demes. This was done in order to obtain an approximation of the migration patterns across the species distribution range. Second, we used a smaller dataset (34 samples), including samples from Hungary, southern Serbia, North Macedonia, and southern Albania using a grid of 500 demes. This was carried out to obtain an approximation of migration patterns in central Europe, where the distribution of the species was the most scattered but the best sampled.
EEMS' Markov chain Monte Carlo (MCMC) algorithm was run for 10,000,000 steps discarding the first 50% as burn-in and saving every 49,995th step to estimate the migration parameters. Two independent runs were carried out to assess the convergence of the MCMC runs. The habitat polygon per dataset was obtained using Google Maps API v3 Tool (http://www. birdtheme. org/useful/v3tool. html), and results were plotted using the R package rEEMSplots as suggested in Petkova et al. [44].
Selection of Environmental Variables We used current climate data  from the WorldClim Version 2 dataset  [45]. The environmental variables encompassed average monthly mean and maximum temperature ( • C), precipitation (mm), wind speed (m•s -1 ), water vapor pressure (kPa), solar radiation (kJ•m -2 •day -1 ), and the 19 WorldClim bioclimatic variables at 30 arc-second resolutions (Table  S2 ). The values of the WorldClim variables at the sample locations were extracted from the raster dataset (extract function in the R package raster).
Correlation Analysis of Environmental Variables Because multicollinearity of variables can result in erroneous modeling  [46] , we removed highly correlated environmental predictors. The variance inflation factor (VIF) was calculated for each environmental variable to detect the presence of collinearity  [47]  with a cut-off value of VIF < 5 following Stucki et al. [48]. The VIF was calculated using the R package fmsb  [49]. To determine which environmental variables contributed most to the overall variation in the individuals' distribution, we run a PCA considering the 7 selected variables and the 53 individuals using the prcomp function in R software. Data were scaled to account for differences in units among each variable.
Samßada Analysis Samßada (v. 0. 6. 0) modeled the probability of a genotype occurrence in an individual according to its habitat's environmental composition using logistic regressions while taking population structure into account  [48]. First, the null model of dimension P, including the population variables and genotype data, was computed for each genotype. To describe the population variables, we used the eigenvalues of the PCA's first 3 principal components (PC1-3), explaining 41. 05% of the total variation in the D. caspius dataset. The effect of each environmental variable was tested by adding one environmental variable at a time to the population variables (dimension P + 1) and assessing which of the 2 models (without or with the environmental variable) was more likely. For each tested model, Samßada created an output file, which contained the model parameters for each genotype, including log-likelihood values.
Following the Samßada user manual's instructions, we calculated a G-score for each tested model from the log-likelihoods of dimensions P and P + 1 (G = 2 × (l P+1l P )). Finally, p-values (P) were calculated and corrected for multiple comparisons using the Benjamini and Hochberg procedure  [50]. The workflow diagram (Supplementary Figure  S1 ) and detailed bioinformatics analyses are provided in the Supplementary Materials. The ddRAD sequence reads adjacent to the SNPs identified to be under selection were mapped with BLASTN v. 2. 2. 31+  [51]  to the Thamnophis sirtalis 6. 0 genome assembly (GenBank: GCF_001077635. 1). Protein-coding genes containing SNPs were determined by using the NCBI Eukaryotic Genome Annotation pipeline's annotation release 100 for the T. sirtalis genome, which was based on sequence homology between a multitude of taxa (https://www.
ncbi. nlm. nih. gov/genome/annotation_euk/process/) and also ab initio models and thus produced in silico functional annotations also for more distantly related (non-model) organisms. Similar annotation pipelines have been applied to a wide range of genetic distances, from distant members of the same clade to individualized assemblies of the same species  [52]. Described functions of protein-coding genes were inferred from GeneCards ® database (www. genecards. org).
Population Differentiation AMOVA of the total dataset subdivided into eight populations showed that the majority of genetic variation was detected among all individuals (50. 8%), followed by variation between populations (41. 5%) and then among individuals within populations (7. 7%). The mean nucleotide diversity (π) within the different populations ranged from 0. 067 (S-HU) to 0. 168 (BALK-ANAT) (Table  S3. Population inbreeding coefficients (F IS ) were significantly different than zero only in the DAN population (F IS = 0. 44; p < 0. 001) and in the C-BAL population (F IS = 0. 121; p < 0. 04), albeit the latter was small (Table  S3. Pairwise F ST values ranged between 0. 132 and 0. 595 (p < 0. 05), with the highest differentiation levels observed between S-HU and N-HU population, followed by S-HU and C-BAL, while moderate pairwise genetic distances were found between the N-HU and DAN, BALK-ANAT and C-BAL populations (F ST = 0.
Estimated Effective Migration among D. caspius Populations Looking at the complete dataset, we observed no large migration corridor across the distribution area (Figure  3A ). However, we observed small patches of possible migration corridors with effective migration rates higher than the overall mean. Higher-than-average effective migration rates were observed within CRI-BES, and between the I-CR. On the other hand, we observed a migration barrier that coincided with the Dinaric Alps. Lower-than-average migration was observed along the Danube (DAN), within the C-BAL clade, and between C-BAL and BALK-ANAT clades. When restricting the analysis to comprise only of the samples from the Carpathian Basin and from the Northern and Central Balkans, the results suggested similar patterns (Figure  3B ).
Conclusions and Conservation Management Recommendations With a comprehensive data set of 17K genome-wide SNPs and samples covering a significant part of its global distribution range, we investigated phylogeography, gene flow, and adaptive selection in D. caspius. We attempted to distinguish between selection signals and demographic processes by employing multivariate analysis as implemented in the Samßada software. Limitations with the number of samples from each location did not allow us to test more specific models of demographic processes, e. g. , based on coalescent analyses with approximate Bayesian computation. We are currently working on a sampling strategy to fill sampling gaps (in space and number), e. g. , including locations north of the Danube River in southern Romania and along the Mediterranean coast, which might help us to understand the current high differentiation of the northern Hungarian (N-HU) population and to systematically follow the (re-) colonization routes of D.
Acknowledgments: We are grateful to  A. Halász ,  A. L. Péntek ,  B. Vági ,  M. Bellaagh ,  M. Tuschek ,  T. Fehér ,  T. Németh ,  V. Szőke ,  V. Krízsik  and  Z. Korsós  for sample collection or technical help in the collections and laboratories. Collection and study permits in Hungary were provided by the  Government Office of Pest County  ( OKTF-KP/6903-21/2015 ),  Danube Ipoly National Park Directorate  ( DINPI/2159-3/2020 ) and  Danube-Dráva National Park Directorate  ( DDNPI 2038-5/2019 ,  DDNPI/2459-4/2018 ). Collection and study permits in Croatia were provided by the  Ministry of Environment and Nature Protection  ( KLASA UP/I-612-07/14-48/76 ,  URBROJ 517-07-1-1-1-14-4 ). The field work on the territory of the  Crimean Peninsula  was carried out in the framework of scientific topics of the  Karadag Scientific Station-Nature Reserve State Assignment No.
Funding: This research was funded by the  Scientific & Technological Cooperation  grant ( HU 02/2018 ) from the  Austrian Agency for International Cooperation in Education and Research (OeAD)  and the Project No. 2017-2. 2. 4-TÉT-AT-2017-00002 , implemented with the support from the  National Research, Development and Innovation Fund of Hungary , financed under the TÉT-17-AT funding scheme. S. L. and J. P. E. acknowledge funding from the  Austrian Science Fund (FWF)   P29623-B25  (to P. A. B. ), while D. J. was funded by the  Slovak Research and Development Agency  under the contract No. APVV-15-0147  and  APVV-19-0076  and D. H. was supported by the  National Research, Development and Innovation Office (NKFIH) of Hungary  (grant no. K-124375 ) and the  Young Investigators Programme (FiKut) of the Hungarian Academy of Sciences (MTA).
and run. The risk-ranking framework included eight criteria: five to describe the severity, three to describe the likelihood. All of them were informed by literature data and food safety agencies' reports, plus experts' opinion. The PROMETHEE outranking MCDA technique, available in R package, was implemented. This risk-ranking framework applied to the results of our smallscale survey revealed that consuming nuts on a regular basis could be the emerging dietary habit presenting the highest-risk score, due to the aflatoxin B1 hazard and its associated health effect (liver cancer). This risk-ranking framework requires however to be applied furthermore in other contexts to evaluate its robustness and identify opportunities for improvement. Once consolidated, this framework will be highly relevant for food safety authorities and policy makers to move forward transparent and evidence-informed decisions.
2-2-Generating the data to populate the framework 2-2-1-Identifying new dietary practices An online survey was conducted using the SurveyMonkey tool: https://fr. surveymonkey. com/. SurveyMonkey has been operating in the EU for a number of years; the international headquarters are based in Dublin, Ireland. SurveyMonkey process respects the General Data Protection Regulation (GDPR) in force in Europe. It is reasonably cheap, the questionnaire is easy to set up and the survey is an on-line one. These four criteria led us to select this company. The survey was intended to the adult French population over 25 years old. It was published on French consumer's online forums such as "60 Millions de Consommateurs". Questions were closed-ended with single or multiple choice. The questions were oriented towards potentially risky practices based on INCA3 conclusions  (Anses, 2017)  and co-author's opinion.
The Probability of sequelae score was "low" when associated to no sequelae, "medium" in the case of possible sequelae, "high" when sequelae are likely to occur. The concerned population or "target population" by the health effect was assessed using ICMSF (2018) or by consensus among co-authors when the information was missing; the Target population score was "yes" when the general population was concerned, "no" when only a specific population was concerned. For the likelihood, three criteria were informed: Strength of the link between habit/food and the hazard, Number of people adopting this habit, Percentage of people who "always" or "often" follow this habit (Table  1 ). Data from the RASFF Portal were analysed to highlight the number of notifications related to biological and chemical hazards.
More generally, consumer's decisions are influenced, among other things, by health, economic, environmental, social, and ethical considerations. To develop our risk-ranking framework, the PROMETHEE method  (Brans et al. , 1986) , that has been used and advocated by FAO to classify risks related to food safety (FAO, 2017), was privileged. Besides, this approach is available as a package in R and is easy to implement. The general principle of outranking methods is to rank alternatives two by two based on criteria defined by the user and weights assigned to these criteria. Outranking methods enable to compare alternatives even if the criteria are semi-quantitative (e. g. low, medium, high) which is very convenient in the public health domain where the scoring system results at least partially from experts' opinion.
3-2-2-Hazards and health effects for each dietary habit For each practice dietary habit, hazards were identified, and for each hazard, health effects were listed. For the first five most frequent dietary practices, a total of 41 Practice-Hazard-Health effect triplets with a large variety of hazards (microbes, parasites, mycotoxins, allergens, other chemical compounds) and health effects (foodborne pathogen disease, anaphylaxis, cancer, immunosuppression, endocrine disturbance…) was obtained. These first five dietary practices were used to validate the risk ranking framework. The associated data are presented in Table  1. For the most popular dietary habit within our 301-sample, i. e. the consumption of seeds, 10 hazards and 13 Practice-Hazard-Health effect triplets were identified. Regarding the emerging consumption of nuts and plant milks, 11 and 2 triplets were identified, respectively.
SMA dust polarization observations The observations (taken in both compact and sub-compact configurations), data reduction and polarization maps of the first 12 low-mass Class 0 protostars observed are presented in  Galametz et al. (2018). The observations of the 8 additional low-mass protostars were obtained with the SMA settled in compact configuration in the 345 GHz band (Project 2018B-S015, PI: A. Maury). The antennas used for each observation date are listed in Table  2. The polarimeter on the SMA makes use of a quarterwave plate (QWP) in order to convert the linear polarization into circular polarization. The antennas are switched between polarizations (QWP are rotated at various angles) in a coordinated temporal sequence to sample the various combinations of circular polarizations on each baseline.
Data reduction, self-calibration, and flux calibration We performed the data reduction on the raw visibilities using the IDL-based software MIR (for Millimeter Interferometer Reduction). The calibration includes an initial flagging of high system temperatures T sys and other incorrect visibilities, a bandpass calibration, a correction of the cross-receiver delays, and a gain and flux calibration. The various calibrators observed for each of these steps and the list of antennas used for the observations are summarized in Table  2. Data were then exported to MIRIAD  (Sault et al. 1995)   1  for additional processing (i. e. , additional flagging) and in particular to perform the instrumental polarization calibration. Quasars were observed to calculate the leakage terms. The continuum data of the targets were used to perform an iterative self-calibration of the Stokes I visibilities.
The process was repeated with deeper cleans and shorter intervals until it converged (no rms improvement). We finally used the Atacama Large Millimeter/submillimeter Array (ALMA) c Mean polarization fraction defined as the unweighted ratio between the mean polarization over total flux. calibration source catalog 2  to gather the fluxes of quasars we also observed with the SMA  (3C 84, 3C 454. 3, 3C 273, and 3C 279)  at similar dates as those of our observations. These fluxes were compared to the SMA amplitudes in order to derive the multiplying factors that were to be applied to the target visibility amplitude in order to flux-calibrate the dataset. These scaling factors are reported for each date in Table  2. The visibilities covered by the observations range from 15 to 85 kλ.
Kinematic properties of the protostellar envelopes The CALYPSO sources. For sources that are part of the CALYPSO sample (i. e. , L1157, L1448C, L1448N-B, L1448-2A, IRAS4A, IRAS4B, and SVS13-B),  Gaudel et al. (2020)  recently presented observations of the dense gas kinematics using C 18 O and N 2 H + measurements. They derived specific angular momentum estimates throughout their collapsing protostellar envelopes from 50 au to 10000 au scales. In their analysis, velocity maps are produced from a combined Plateau de Bure (PdBI) + 30-m N 2 H + dataset. A hyperfine structure line profile was used to determine the velocity of the molecular line emission in order to produce a velocity map. Then velocity gradients were fit in a 40 × 40 region around the sources and determined by the least-squares minimization, with v grad = v 0 + a∆α + b∆β, with ∆α and ∆β the offsets with respect to the central source  (Goodman et al.
Uncertainties also remain about the nature of potential companions detected in HH211 and L483, hence these sources appear with two colors in Fig. 3. In order to assess whether the two populations (single versus multiple) belong to the same distribution, we applied a 2D Kolmogorov-Smirnov test. We used the two python scripts ndtest. py 4 and KS2D. py 5  to estimate the K-S statistics and p-values. These 2D testings are based on statistical methods developed by  Peacock (1983)  and  Fasano & Franceschini (1987). Excluding the sources for which the exact multiplicity nature is unsure (i. e. , HH211 and L483), both methods return the same low p-value of 0. 13, indicating that the single and multiple source populations likely do not belong to the same population. More sources would, however, be necessary to reinforce this statistical test.
Projection effects The position and misalignment angles quoted in this analysis are projected in the plane of the sky and might differ from the real angles between the various directions analyzed (velocity gradients, B, and outflows). In order to assess the uncertainties linked with projection effects, we performed a Monte Carlo test. We developed a python script that randomly generates a 3D plane. This plane was our plane of the sky. We then generated 10000 random pairs of vectors in this 3D space. For each pair, vectors were projected onto the 3D plane (using the orthogonal basis of the plane) to estimate the two 'projected vectors' and thus 'observed angle' between the two. The 'real' and 'observed' angles were then compared. Figure  5  shows the density distribution of the observed versus real angles.
Acknowledgments We thank the anonymous referee for his/her constructive suggestions that improve our methodology descriptions and overall quality of the manuscript. This project has received funding from the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation programme (MagneticYSOs project , grant agreement N • 679937, PI: Maury). J. M. G. is supported by the grant  AYA2017-84390-C2-R (AEI/FEDER , UE). We thank  John Tobin  for providing us with the  BHR7 H 2 CO  and N 2 D + datacubes (presented in Tobin et al. 2018). We also thank  Naomi Hirano  and  Brenda Matthews  for providing us with the N 2 H + data cubes for B1b and B1c respectively (data presented in Huang & Hirano (2013) and Matthews et al. (2008)). We finally thank  Aina Palau  for providing us with the C 18 O data cube for HH797 (see Palau et al.
We thus took the opportunity of the EU Horizon 2020 (H2020) HIRMEOS 8 project to further consolidate and expand the service. Indeed, HIRMEOS addresses the peculiarities of academic monographs as a specific support for scientific communication in the social sciences and humanities. It aims to prototype innovative services for monographs by providing additional data, links to, and interactions with the documents, at the same time paving the way to new potential tools for research assessment, which is still a major challenge in the humanities and social sciences. The introduction of entity-fishing has undergone different levels of integration. The majority of the participating publishers provided additional features in their user interface, using the data generated by entity-fishing, for example, as search facets for persons and locations to help users narrow down their searches and obtain more precise results.
On a larger scale, with an increasing number of documents, the resulting graph of interlinked connections allows the computing of aggregated information such as trends, semantic search, or document similarity. Authors making the choice of open-access publishing may also have a particular interest in having their work more frequently discovered, read, used, and reused to get credit and recognition. Therefore, it is the duty of the corresponding infrastructure to provide the means to accomplish these objectives. HIRMEOS is a great opportunity to improve the current open access technical infrastructure at the European scale. In following subsections we describe the entity-fishing service. Sections 4. 1 and 4. 2 provide background information; then we describe the architecture and data flow in section 4.
The mention extraction is performed by means of generic parsers (known as NER, Named Entity Recognition) or by plugging in specialized parsers when dealing, for instance, with dates, ancient names, or chemical formulas. The entity disambiguation task (also called entity linking, named entity disambiguation, named entity recognition and disambiguation) consists of determining the actual identity of the entity which is referred to by expressions appearing in a document. There is a long-standing body of research that aims to improve efficiency and completeness of the extraction of relevant information and senses depending on the context provided by the text, as well as the reference background provided by largescale entity databases such as Wikipedia  (Cucerzan 2007). In fact, entity disambiguation requires a knowledge base that contains all the entities to which each mention may be linked.
With its open license, Wikipedia has become the reference knowledge base  (Milne, Witten, and Nichols 2007)  for such a task. Ratinov et al. (2011)  formally define Wikification as the task of identifying and linking expressions in text to their referent Wikipedia pages. In order to be more generic, mentions can be recognized using several techniques (based on Machine Learning [ML], lexicons, or rules), extended to any kind of expression beyond the narrow notion of "named entities. " However, since Wikipedia-although comprehensive-has some sort of physical limitations, it could happen that certain entities identified in a text are not found at all in the knowledge base. More details about Wikipedia and Wikidata will be discussed in section 4. 5. To illustrate this phenomenon, we can take the following text as an example:.
President Obama is living in Washington There are two mentions, President Obama and Washington, in the sentence. If we consider our approach based on Wikipedia, the entity-fishing knowledge base terms lookup will say that there are respectively 10 possible candidates for Obama and 715 for Washington. A 13 DBpedia page, accessed July 24, 2020, http://dbpedia. org/page/Entity. correct disambiguation will be the link of the mention Washington to the entity titled Washington D. C. , described as "Washington, D. C. , formally the District of Columbia and commonly referred to as Washington or D. C. , is the capital of the United States of America" 14. and identified by Wikipedia page ID 108956 and Wikidata ID Q61. Moreover, the mention Obama should be correctly disambiguated to Barack Obama with Wikipedia page ID 534366 and Wikidata ID Q76.