dataesr/scientific-paragraphs-categorization huggingface dataset:
Social drivers Of all the categories of drivers identified in this study, datasets for social drivers were the most difficult to source. This is primarily due to the fact that there are no applicable social datasets on PANGAEA, which is by far the largest provider of data in this project (Table  2 ). Of the hundreds of datasets sourced, only 28 of them provide social data. A total of 3 provide monthly tourism values over the past several years for Kongsfjorden and/or Isfjorden (Table  2 , Fig. 3 ), 1 provides ship Automatic Identification System (AIS) data in western Svalbard, 10 provide data for monthly governance statistics for Greenland, and another 8 provide data for Norway. Fisheries are a very well-quantified driver, with a very well-established body of statistical analyses for comparison with the natural world.
Six such datasets were sourced via national statistics websites for Greenland, and three were sourced for Norway. There are 19 fishery datasets available via the IMR site (https://gbif. imr. no/ipt/, last access: 17 August 2023), three of which have been amalgamated. More so than with the other categories of data, there is overlap in the variables for the drivers within this category. For example, boat traffic within the fjords is an important social consideration but must be classified either into the tourism or fishery drivers depending on the ship in question. The variable names are otherwise the same, which required that the reference in question be consulted in order to accurately rename them  (i. e. vesselstourism [n] vs. vesselscommercial [n] ). The division and regrouping of these variables was by far the most time consuming of all of the categories due to how many small exceptions there were.
Relationships between drivers In order to illustrate the potential uses of this dataset, a comparison of the different drivers is outlined below. This is not an exhaustive comparison nor is it meant to be proscriptive in terms of the use of these data. That would depend on the question(s) being asked by a given researcher. It must also be noted that any values determined in a course analysis of these data do not necessarily reflect the changes that exist within the fjord. We have performed large aggregations of the data here in the interest of simplifying the analysis. Additionally, it would also not be useful to compare every driver in this dataset to each other. Rather it is necessary to follow a guiding principle for which drivers are compared and why. This is found in Fig. 2  of  Schlegel et al.
(2023) , which shows the key relationships between drivers and the direction of their interactions, as determined from the literature. Because the aim of this dataset is to allow for investigations of the interactions between drivers within a given Arctic fjord socioecological system, we did not compare data across sites. In order to broadly quantify the comparisons that can be made for drivers within fjords, the data have been binned into a few consistent depth ranges and averaged into monthly means. Time series with only annual values were not used as these created skewed comparisons against other drivers for just the months of January or December accordingly. Finally, comparisons were only made when there were at least 3 months of overlapping data, and only data from 1982 to 2020 were used as this is the period available for the CCI SST product.
It must be noted that, even though the methodology used for data comparison is coarse, there are still many drivers with either no overlapping monthly values or only a couple of sites that have overlap. For many sites there are almost no drivers that overlap with anything other than seawater temperature (Table  3 ). Of the 217 relationships that exist between the variables within drivers, only 1 was able to be quantified across all of the seven sites contained within this dataset (Table  3 , Fig. S1 ). A total of 18 comparisons could be made between just two different sites, 7 could be made between three sites, and 7 more comparisons could be made between four or more sites. For drivers comparable between at least four or more sites, the mean (median) number of overlapping months was 82 (54), with a minimum and maximum range of 6 to 360 months.
Code and data availability As detailed above, certain decisions were made about which variables to group into which categories and about drivers that may not be agreed on by all researchers. Regardless, due to the meta-data columns attached to each datum in this data product, it is possible to quickly isolate which data are of interest and extract them. For research projects making heavy use of data from a limited number of references for data within this product, it is advised that these sources be cited in addition to the citation of this larger data amalgamation. This advice is similar to that for the use of data within the SOCAT  (Bakker et al. , 2016)  and GLODAP  (Lauvset et al. , 2022)  datasets. The live code base within which the sourcing, cleaning, and amalgamation of this dataset occurs may be found on GitHub at https://github.
com/FACE-IT-project/WP1 (last access: 17 August 2023). Please note however that this is an ongoing process, and the code has already changed in anticipation of v2. 0 of the dataset. That being said, the code used for the analyses, figures, and tables seen in this publication was published in its original state and is available at https://doi. org/10. 5281/zenodo. 8263692 (Schlegel, 2023). A meta-database providing a high-level summary of the individually sourced datasets in this data product (i. e. not scraped from PANGAEA) is available here: https: //face-it-project. github. io/WP1/metadatabase. html (last access: 17 August 2023). A user interface (UI) for the live version of the data product may be accessed on the same website by clicking the "Data access" tab. The full data product is published on PANGAEA at https://doi.
It must be noted, however, that most of these analyses across sites are aided by the inclusion of remotely sensed seawater temperature and sea ice cover data. Without these, the out-of-the-box applicability of this amalgamated data product to Arctic research would be reduced. Within the individual sites, however, there are enough in situ collected data for many interesting analyses. The in situ collected data for many of the drivers in this data product required additional filtering (e. g. terrestrial runoff) and in some cases the conversion of the units of measurement (e. g. carbonate system and nutrients). Most of the data classified into the biology drivers also required careful consideration as to how best present the raw data to the user while still maintaining a consistent project-wide standard for this dataset.
This necessarily required the calculation of a species richness value, which was not present in any of the sourced datasets but will be further improved upon in later versions of this data product. This data product represents the first version of a data collection effort that will be ongoing for the next 2 years. Central to this process is the expansion of efforts to collect biological and social datasets, which are currently underrepresented here. The quantification of interview data is also something that is being investigated and will be addressed in the future version of this dataset. The monitoring of the primary online databases that have contributed to this project is ongoing, and as datasets therein are updated, they will be amalgamated here. The future versions of this dataset will also be published on PANGAEA, with backward references to this first version, as is standard practice.
There are two important data sources referred to in this paper whose data cannot be shared directly in this product due to data access restrictions. Almost the entirety of the terrestrial runoff data are found in the GRDC (Global Runoff Data Centre; https://www. bafg. de/GRDC/EN/04_spcldtbss/41_ARDB/ardb_node. html, last access: 17 August 2023). Additionally, almost every source of coastal (non-social) data for Greenland is stored on the Greenland Ecosystem Monitoring database (GEM; https://data. g-e-m. dk/,last access: 17 August 2023). No full socio-ecological analysis of Arctic fjords can be conducted without data from these sources. A table containing the list of categories, drivers, and the cleaned names for the individual variables within this dataset is available at https://github. com/FACE-IT-project/ WP1/blob/main/data/analyses/table_A1.
Acknowledgements. This study is a contribution to the project  FACE-IT (The Future of Arctic Coastal Ecosystems -Identifying Transitions in Fjord Systems and Adjacent Coastal Areas ). We gratefully acknowledge the input of our FACE-IT colleagues for pointing to relevant data repositories. Special thanks are due to  Morten Skogen  for supplying the NORWECOM model data. Thanks are due to  Jan Marcin Węsławski  and an anonymous referee for their comments on an earlier version of this paper. Financial support. FACE-IT  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 869154. Review statement. This paper was edited by  Giuseppe M. R. Manzella  and reviewed by  Jan Marcin Węsławski  and two anonymous referees.
TMM likely jeopardized the regulatory, structural and production functions of the sourced ecosystems as they sold species with the greatest body weights and generation lengths, and smallest litter sizes, including large herbivores and keystone predators. BM, despite their restricted range, sourced among a non-selective taxonomic and functional spectrum and as such, also represent a significant threat to ecosystem functioning in southern Benin. The functional database that we provide can serve as a starting point to pursue the quantification of functional diversity in African mammals and further investigate the impact of wildlife markets on ecosystem functioning in tropical Africa. Overall, long-term surveys of the two types of wildlife markets are needed to accurately quantify the threat they constitute to mammalian biodiversity and the sustainability of ecosystem services.
, 2015). In this case, we used Blastn (https://blast. ncbi. nlm. nih. gov/) to search the NCBI nucleotide database, optimizing for highly similar sequences (megablast;  Ng and Pang, 2010). We applied the decision pipeline of  Gaubert et al. (2015) : Fig. 2 ), which relies on the taxonomic distribution of the best hit values, distance tree view of the query, assessment of NCBI database coverage and expert validation, as well as similarity threshold values, to taxonomically assign the sequences. Final molecular identification was determined on a 4-gene consensus-based approach, by choosing the most inclusive phylogenetic level (i. e. species or higher taxonomic categories) relative to the level of conflicting taxonomic identifications observed among the four genes  (Gaubert et al. , 2015).
Database A database was established for all the species identified using both morphological and molecular approaches (Table  1 SI ). We reported both global (IUCN RL ) and local (Red List for Benin; RLB) conservation status. The presence or absence of a species in Benin was cross-checked between the two references. Bioclimatic zone coverage was inferred from the IUCN RL range maps. Ecological and functional information on habitat, diet, litter size per year and mean body weight was extracted from  Kingdon (2015)  and the Mammals of Africa  (Kingdon et al. , 2013). Knowledge gaps were completed by taxon-specific search on Google and Google Scholar (see  Table 1 SI). Generation length was extracted from the IUCN RL. Missing data were completed by using the IUCN RL generation length calculator (https://nc.
iucnredlist. org/redlist/content/attachment_files/Generation_Length_Workbook. xls). For this purpose, data on longevity and fertility were extracted from the sources referred to above, and completed using specialized databases (https://genomics. senescence. info/species/index. html; https://www. demogr. mpg. de/longevityrecords/0203. htm). Survival rate was arbitrarily fixed to 0. 5 when unknown (most of the cases).
Descriptive statistical analyses Descriptive statistics for comparing the biodiversity spectrum, conservation status and functional diversity between the species sold in TMM and BM were run in XLSAT 2021. 4. 1  (Addinsoft, 2022). We used the nonparametric Mann-Whitney U test (McKnight and Fig. 1. Distribution of the two types of wildlife markets surveyed across southern Benin. Traditional medicine markets: 1-Zobè ; 2-Dantokpa; 3-Vèdoko; 4-Godomey; 5-Calavi; 6-Gbèdagba; 7-Avogbannan. Bushmeat markets: 8-T è gon (at the vicinity of the Lama Forest); 9-Sèto; 10-Hounkpogon. Najab, 2010) to test differences in distributions between TMM and BM for taxonomic richness, global and local conservation status, habitats, diet and functional categories. None of the differences proved significant (data not shown).
DNA-typing showed that these artifacts were from domestic dogs (skull and skin). multidimensional functional space  (Legendre and Legendre, 2012). According to the mSD metric  (Maire et al. , 2015) , which quantifies the quality of the functional space, we kept the first five axes of the PCoA accounting for about 70% of the total inertia. The mFD package was used in R (https://cran. r-project. org/web/packages/mFD/index. html) to compute the functional diversity metrics  (Magneville et al. , 2022).
Six morpho-species could not be identified because of (i) the non-resolutive power of mtDNA (large-spotted genets: Genetta pardina or G. "maculata"; cats: Felis silvestris or F. catus), (ii) among-gene conflicting identities (antelope: Kobus spp. ), and (iii) lack of reference sequences in databanks (rodents: Anomalurus sp. , Funisciurus sp. , Heliosciurus sp. ). The samples of dry heads from "leopard" and "wild dog" collected in TMM returned as domestic dogs (T2212 to T2214; Table  2.
However, the species' ranges roam at the vicinity of southern Benin, and their -yet unreported-occurrence in the country remains conceivable (see  Cassola, 2016; Gerrie and Kennerley, 2016). Despite our multiple-evidence approach, four taxa could not be genetically identified to the species level, including an antelope (Kobus), a scaly-tailed squirrel (Anomalurus), an African striped squirrel (Funisciurus) and a sun squirrel (Heliosciurus). Because cryptic diversity is likely affecting several groups of small to medium-sized mammals in tropical Africa and notably in the Dahomey Gap (e. g. ,  Colyn et al. , 2010; Gaubert et al. , 2016; Oates et al. , 2022) , further investigations will have to be conducted to decipher whether these represent new taxa or yet unrepresented species in public databases.
Our study had the merit to highlight for the first time the potentially deleterious impact of the wildlife trade on the FD of mammalian communities in western Africa. Together with taxonomic diversity, FD is a marker of evolutionary heritage and ecosystem productivity  (Ahumada et al. , 2011; Ernst et al. , 2006; Oliveira et al. , 2016 ) that needs to be considered when tackling the sustainability of the wildlife trade. On the basis of the taxonomic and functional database that we provide, it will be important to further quantify FD in African mammals using continuous traits, given the existing knowledge gaps and the promise of such traits to better apprehend FD  (Kohli and Rowe, 2019). Such dedication will ultimately help investigate the appropriate scales to which FD metrics may reflect changes in ecosystem functioning in western Africa (see  Hatfield et al.
Funding The survey was funded by the program  Jeune Equipe Associée à l'IRD  ( RADAR-BE ) and the  Laboratoire Mixte International SHUNT (LMI IRD). Lab work was supported by  PANGO-GO  ( ANR-17-CE02-0001 ) and  BUSHRISK  ( FCT IC&DT 02/SAICT/2017-n • 032130 ). SZ was funded by a  PhD grant   ARTS-IRD.
Data Availability Statement The experimental data sets are available in Aichele (2022, https://zenodo. org/record/6886498) as *. mat and can be read with Matlab or scipy (python). The code to reproduce the simulations is available under Aichele (2022, https://zenodo. org/record/6886498) and JoTrab (2022, https://zenodo. org/record/7092715). Codes are confirmed to run under MATLAB (2020).
Discussion In summary, we first build a daily state-level power generation dataset for U. S. states and calculated the daily state-level carbon emission and carbon intensity. Based on this state-level dataset, we could quantify the impact of extreme events on the energy production and specifically impact of extreme cold events and hot events, on U. S. carbon emissions and carbon intensity. Finally, we further analyze the extreme events' intensity (defined by air temperature changes) and duration (consecutive days) of the two types of extreme events to provide a direct comparison between extreme hot days and cold days. Our analysis mainly demonstrated that, 1. We found sharp increases of carbon intensity during extreme cold and hot days. As many studies reported increasing frequency and intensity of extreme events, our results suggest that more fossil emissions will be released in the future from extreme events.
Acknowledgements P. G. would like to acknowledge funding from  European Research Council  grant  USMILE and National Science Foundation Science and Technology Center LEAP, Learning the Earth with Artificial intelligence and Physics , as well as support from  NASA  grant  80NSSC18K0998. W. Z. would like to acknowledge the support from the  Carbon Monitor  project (https://carbonmonitor. org/) and the scholarship awarded by the  Max Planck Institute for Biogeochemistry. The authors also thank  Julianne DeAngelo  for help analyzing AR6 scenarios.
Data availability The real-time hourly balancing authority-level electricity net generation dataset by energy source was downloaded from the U. S. Energy Information Administration (EIA) on-line database (https://www. eia. gov/opendata/qb. php?category=3390101). The population density data is from the GPWv4 dataset (https://sedac. ciesin. columbia. edu/ data/set/gpw-v4-population-density-rev11). The air temperature data are from ERA5-Land reanalysis datasets (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview). The processed dataset can be obtained from the following link: https://doi. org/10. 5281/zenodo. 10161493.
Available data The most detailed -even though large-scale -existing geological map for the Pinchal area is the 1 : 250 000 Quillagua map  (Skarmeta and Marinovic, 1981). For the Quebrada Blanca area, the recent 1 : 100 000 Guatacondo map  (Blanco and Tomlinson, 2013)  provides detailed and updated information on the stratigraphy and structure. There, the structure of the folded Mesozoic rocks has been preliminarily mapped and qualitatively described by other authors  (Armijo et al. , 2015; Blanco and Tomlinson, 2013; Fuentes et al. , 2018). Enhanced cartographic details can be deduced from highresolution satellite imagery. We use Google Earth imagery (Landsat 7, DigitalGlobe) whose resolution varies from a few meters to a few tens of meters depending on the zones. In addition, this work benefits from very high-resolution imagery from the European Pléiades satellites.
Using the MicMac software suite  (Rosu et al. , 2014; Rupnik et al. , 2016) , we calculate high-resolution digital elevation models (DEMs) from tri-stereo Pléiades imagery with a 0. 5 m resolution. These DEMs are down-sampled to a resolution of 2 m to enhance data treatment and calculations (e. g. , stratigraphic projection and image processing). Relative vertical accuracy may reach ∼ 1 m, depending on local slope. Field observations were acquired during two field surveys in March 2018 and January 2019. Difficult accessibility and field logistics in the remote and desert Pinchal area only allow detailed complementary field observations of a relatively limited area. Observation points and the off-road track followed to reach our field site in the Pinchal area are provided in the Supplement.
g. ,  Allmendinger, 1998; Erslev, 1991). We use the code FaultFold Forward (version 6)  (Allmendinger, 1998)  in order to jointly model thrust displacement and anticlinal folding. Trishear models the deformation distributed within a triangular zone located at the tip of a propagating fault  (Erslev, 1991). This forward modeling relies on testing a set of parameters, namely the position of the fault tip, the angle of the propagating fault ramp, the slip on the fault, the propagation-to-slip ratio (P / S) of the fault, the angle of the triangular zone at the tip of the fault where distributed deformation occurs (i. e. , the trishear angle), and the inclined shear angle controlling the backlimb kinematics. We assume here the case of linear symmetric trishear to keep models as simple as possible, meaning that folding of the backlimb occurs parallel to the fault.
esa. int/ eogateway/missions/pleiades, last access: March 2018) was obtained through the ISIS program of the CNES under an academic license and is not available for open distribution. On request, the DEMs calculated from this imagery can be provided to any academic researcher, but only after approval from the CNES (contact: isis-pleiades@cnes. fr, with copy to lacassin@ipgp. fr and simoes@ipgp. fr referring to this paper). Numerical computations for the DEMs were performed using the free and open-source MicMac software suite  (Rosu et al. , 2014; Rupnik et al. , 2016)  freely available at https://micmac. ensg. eu/index. php (last access: April 2018). For cartographic mapping, we also used Google Earth imagery (Landsat 7, DigitalGlobe) freely accessible at https://earth. google. com (last access: July 2021).
Acknowledgements. This study was supported by grants from the  CNRS-INSU (program TELLUS-SYSTER ) and from the  Institut de physique du globe de Paris (IPGP)  (PI:  Robin Lacassin ). Fieldwork was also funded by the  Andean Tectonics Laboratory of the Advanced Mining Technology Center, University of Chile  (PI:  Daniel Carrizo ). Earlier work on this zone by  Robin Lacassin  and  Daniel Carrizo  was supported by  ANR  project  MegaChile  (grant.
Residents are paying rent for very small and often crowded rooms (Supplementary Notes 3). We estimated sample size per stratum, based on assumptions reflecting vaccine hesitancy reported in the literature, and considering 80% power, design effects of 3% and 5% accuracy. Total estimated required sample size was 3751 (Supplementary Methods 1). In Ile-de-France, we built sampling frames for each stratum using data provided by various actors involved with these populations; these listed locations, and the size of each site (Supplementary Methods 1). In each stratum, recruitment sites were randomly selected (first stage) proportionally to their size. Sample size per site was calculated in proportion to the expected site population, with participant sampling in the second stage depending on site type.
In Marseille, a local NGO record all PEH/PH and migrants living in the urban area to provide healthcare and legal advice. This NGO was the operational partner for the study. We drew a subsample from their cohort, using simple random sampling, with planned replacements for refusal/absence. Outcomes and definitions. Vaccine uptake was the main outcome, defined as the uptake of at least one COVID-19 vaccine dose, irrespective of brand or type. Vaccine coverage was defined as a full schedule of COVID-19 vaccine, i. e. at least two doses of messenger RNA vaccine (usually Pfizer), one dose of Janssen vaccine, or one dose of any vaccine following prior to COVID-19 infection. Data collection. After obtaining verbal informed consent, questionnaires were administered by trained interviewers in the participant's preferred language.