dataesr/scientific-paragraphs-categorization huggingface dataset:
Data are computed by presenting each of the 31 possible cooperative partners to the focal agent for 100 iterations as policies are stochastic. The green vertical line represent the mean investment of the focal agent. We studied the dynamics of two reinforcement learning methods: a gradient policy search algorithm and a direct policy search algorithm with an evolution strategy. Both algorithms succeeded in learning policies that make an optimal use of partner choice when interaction opportunities are frequent. However, the two algorithms differ fundamentally when interaction opportunities are rare. The direct policy search algorithm shows total robustness, while the gradient policy search algorithm collapses, resulting in sub-optimal policies. The robustness of the direct policy search method can be expected as the sequential and temporal aspects of the task is lost within one evaluation.
Figure7: Analysis of the Partner Choice module for all conditions (by columns:  ∈ {0. 1, 0. 2, 0. 5, 1. 0}) and all algorithms (top: CMAES, center: PPO-DEEP, bottom: PPO-MLP). For each setup, only the best policy is shown. Each graph plots the probability to accept cooperation for the focal agent following the best policy (y-axis) depending on its partner's proposed investment (x-axis). Data are computed by presenting each of the 31 possible cooperative partners to the focal agent for 100 iterations as policies are stochastic. The green vertical line represent the mean investment of the focal agent.
Dust extinction Dust in the galaxy absorbs the radiation at short wavelengths, especially from the UV to the near-IR; this energy is then reemitted in the mid-to far IR. As HyperGal is primarily targeting sources at redshift z < 0. 1 in the optical domain, the extinction effect is properly considered through the dust attenuation module dustatt_modified_CF00 from  Charlot & Fall (2000). This approach is considering two star populations: the young ones (<10 7 yr) still reside in their birth cloud (BC), and the old ones are considered as already dispersed in the interstellar medium (ISM). Attenuation is therefore treated differently: for the young population, both ISM and BC are considered, while for the old population, only the ISM is considered. In both case, the attenuation A λ is modeled by a power law, normalized by the V-band attenuation: A k λ = A k V λ λ V n k k = BC or ISM, (4) with λ V = 0.
Acknowledgements. This project has received funding from the  Project IDEX-LYON  at the  University of Lyon , under the Investments for the  Future Program  ( ANR-16-IDEX-0005 ), and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 759194 -USNAC ). The SED Machine is based upon work supported by the  National Science Foundation  under Grant No. 1106171. Based on observations obtained with the Samuel Oschin Telescope 48-in. and the 60-in. Telescope at the  Palomar Observatory  as part of the  Zwicky Transient Facility  project. ZTF is supported by the  National Science Foundation  under Grant No. AST-1440341  and a collaboration including  Caltech , IPAC, the  Weiz-mann Institute for Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland , the  University of Washington ,  Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories , the  TANGO Consortium of Taiwan , the  University of Wisconsin at Milwaukee , and  Lawrence Berkeley National Laboratories.
Host to intricated networks of marine species, coral reefs are among the most biologically diverse ecosystems on Earth. Over the past decades, major degradations of coral reefs have been observed worldwide, which is largely attributed to the effects of climate change and local stressors related to human activities. Now more than ever, characterizing how the environment shapes the dynamics of the reef ecosystem is key to (1) uncovering the environmental drivers of reef degradation, and (2) enforcing efficient conservation strategies in response. To achieve these objectives, it is pivotal that environmental data characterizing such ecosystem dynamics, which occur across specific spatial and temporal scales, are easily accessible to coral reef researchers and conservation stakeholders alike. Here we present the Reef Environment Centralized Information System (RECIFS), an online repository of datasets describing reef environments worldwide over the past few decades.
, 2016; Lecours et al. , 2021). There are already online repositories that provide synthesized variables of marine environments worldwide, such as Bio-ORACLE  (Assis et al. , 2018; Tyberghein et al. , 2012)  and MARSPEC  (Sbrocco & Barber, 2013). The synthesized variables are derived from remotely sensed data obtained from public initiatives, such as the World Ocean Atlas  (Boyer et al. , 2018)  and the Copernicus Marine Service (EU Copernicus Marine Service, 2022), and describe long-lasting trends in global oceanic conditions. These synthesized datasets are predominantly produced (and widely used) for spatial distribution modelling of marine organisms  (Sbrocco & Barber, 2013; Tyberghein et al. , 2012). However, these repositories are not coral reef-centered, and therefore do not give access to environmental variables that are key to understand the dynamics of the coral reef ecosystem, such as coral heat stress indices (e.
g. , those from the Coral Reef Watch;  Liu et al. , 2014)  or indices of local fishing activities or of human land use along the coastline. In addition, these repositories offer limited possibilities for customizing the spatial and temporal extents of environmental data, where such customization is key when analyzing the relationship with insitu measurements  (Leempoel et al. , 2015; Murray et al. , 2018). For example, a certain in-situ measurement (e. g. , the abundance of a given species; the presence of an adaptive genetic trait) could be driven by an environmental variation during a specific season (e. g. , thermal stress during the hot season), or by pollution-related human activities within a certain distance from the coastline (e. g. , agricultural areas in a 50 km radius). As the sub-setting and processing of environmental data is generally performed using dedicated software (e.
g. , Rpackages, Python), this data processing generally requires competences with geographic information systems (GIS) and scientific programming. The lack of an interactive and easy-touse user interface hampers the integration of these data by non-GIS experts, as may be the case for coral reef conservation practitioners  (Guisan et al. , 2013; Selmoni, Lecellier, Ainley, et al. , 2020). Here, we present the Reef Environment Centralized Information System (RECIFS), an online repository of datasets describing the reef environment worldwide over the past decades. The datasets provided through RECIFS originate from the public domain, and characterize various facets of the reef environment including water chemistry (e. g. , chlorophyll concentration, salinity, etc. ) and physics (e. g. , temperature, heat, velocity, etc.
), as well as anthropogenic impacts (e. g. , population density, land use of agricultural and urban areas) along the neighboring coastlines. The datasets from RECIFS can be accessed at different spatial resolutions and are sub-settable along the temporal dimension (i. e. , select specific seasons and/or specific years). The data are delivered through an intuitive a free-to-use web-GIS application, available at https://recifs. epfl. ch. We hereunder present the functionalities of this tool, and describe two cases studies showing possible implementations: 1) the characterization of coral diversity in the Caribbean, and 2) the study of local adaption of a reef fish population along the northwestern coast of Australia.
Data structure: reef environment The RECIFS repository is based on three types of datasets describing the reef environment retrospectively at different temporal resolutions: monthly, annual and non-temporal (i. e. , variables without a temporal resolution; Table  1 ). These datasets were accessed at a global level as raw data, and were then processed and synthesized as outlined here below (and as summarized in Figure  1 ). Raw datasets with a monthly temporal resolution were stacks of raster images, where every image described a monthly statistic (usually the average) of a given oceanic environmental variable globally. RECIFS includes the raw monthly datasets for the following environmental variables: chlorophyll concentration, degree heating week, iron concentration, oxygen concentration, pH, nitrate concentration, phosphate concentration, sea current velocity, suspended matter concentration, salinity, and temperature.
These variables represent data at near-surface depth, with spatial resolutions ranging between 5-25 km, and across temporal windows generally covering the past 2-3 decades (see Table  1 for   Raw datasets at yearly temporal resolution were also raster stacks, but here each image was a yearly measure of a given variable worldwide. The datasets of this type included in RECIFS were: land cover of built-up and cropland areas (covering the 2015-2019 period, at 100m resolution; Copernicus Global Land Service, 2022), boat detection (2017-2020, at 500m resolution;  Elvidge et al. , 2015 Elvidge et al. , , 2018;; Hsu et al. , 2019) , and human population density Two raw datasets without a temporal resolution were included in RECIFS, both derived from the Global Multi-Resolution Topography Data Synthesis, which is a single raster image describing topography (on land) and bathymetry (on sea) worldwide at ~1 km of spatial resolution  (Ryan et al.
, 2009). The first derived dataset is a depth map where the pixels represent the topography of below sea surface level, and the second is a binary map where pixels were either assigned to land (topography > 0) or to sea (topography < 0). To process these raw environmental datasets, we extracted values from the raster stacks using custom R scripts (v. 4. 1; R Core Team, 2021), featuring the raster  (v. 3. 5; Hijmans, 2021)  and the rgdal  (v. 1. 5; Bivand et al. , 2021)   WCMC et al. , 2021) , and were reported to 5-by-5 km cells. The resulting grid was composed of 61,038 reef cells. For each reef cell, we extracted the environmental values from each raw dataset using a buffer to average over different radiuses (2. 5 km, referred to as "on reef" extraction, and 10 km, 25 km and 50 km). The result was a set of tables (called "Extended tables"), with one table per environmental variable at a given buffer size.
Web-app and queries The web-based application of RECIFS is built on a NodeJS server (v. 10. 13; OpenJS Foundation). On the front-end, the server features an Openlayers (v 5. 3; Open Source Geospatial Foundation) map interface displaying the reef cells grid, while the back-end of the server stores environmental data in a tabular format and the raster images summarizing sea surface currents. When the front-end user defines an "Area of Interest" through the interactive map, the server queries the RECIFS database through R-scripts (featuring the raster and the sp libraries; v. 1. 4; Bivand et al. , 2013) processing, sub-setting and finally returning the data stored on the backend. Three types of query are possible: (1) standard environmental query: the user selects an "Area of Interest", and the server returns precomputed environmental values from the "Summary table" for the reef cells from this area.
The map rendered on the web-interface can be downloaded either in a pdf or a tabular format. Furthermore, the user can define "Points of interest" on the map (e. g. , sampling locations, reefs of interest) and download the environmental data for the closest reef cells to such points. All these functionalities are available in complete open access, without any registration required. Raw environmental data used in RECIFS comes from publicly available repositories, and have distinct temporal resolutions (monthly, annual or no resolution). Shapes of coral reefs worldwide were summarized into a regular grid (reef cell size: 5-by-5 km), and raw environmental values were extracted for each reef cell. These extractions were performed using four different buffer sizes (2. 5 km -on reef, 10 km, 25 km or 50 km) around the centers of reef cells.
The result is a set of tables ("Extended tables") describing variation across reef cells for each environmental variable, at different buffer sizes and through different time periods (months or years). These tables are synthesized into a "Summary table", reporting overall temporal average and standard deviation of each environmental variable, at every reef cell. RECIFS also includes data on sea surface current velocity and direction, obtained from processed raw data describing eastward and northward surface water velocity. These processed data are accessible through the RECIFS web-application: the user selects an area of interest through an interactive map and runs either an environmental query to visualize environmental variation (standard query to access the Summary table, advanced query to access the Extended tables), or a sea current query to visualize sea currents.
Results & Discussion A repository to characterize the reef environment RECIFS is the first repository that synthesizes data from various sources to describe reef environments globally. In comparison to existing repositories of marine environmental data  (Bio-ORACLE, Assis et al. , 2018; Tyberghein et al. , 2012; MARSPEC, Sbrocco & Barber, 2013) , RECIFS has distinctive features to support (1) the study of coral reef ecosystem dynamics, and (2) the organization of reef conservation strategies. The data provided through RECIFS is centered on the reef environment and characterize key environmental constraints of this ecosystem, including conditions that trigger coral bleaching events and represent coastal pollution-related activities. RECIFS data are available under specific spatial and temporal scales, providing pivotal information for researchers investigating how the reef environment shapes ecosystem dynamics  (Melo-Merino et al.
, 2020; Murray et al. , 2018). For instance, modelling the abundance or diversity (e. g. , Box 1) of reef taxa with different characteristics (e. g. , sessile vs. mobile), or facing different types of environmental constraints (e. g. , constant vs. episodic), will require distinct spatial and temporal resolutions of the environmental dataset  (Fernandez et al. , 2017; Mannocci et al. , 2017; Melo-Merino et al. , 2020). The same considerations apply to molecular studies on local adaptation (e. g. , Box 2), as the genetic makeup of sampled individuals is shaped by ancestors exposed to differing frequencies of environmental stressors (i. e. , temporal resolution of variables to be adjusted accordingly), which dispersed over larger or smaller spatial scales (i. e. , spatial resolution of variables to be adjusted accordingly;  Leempoel et al.
Perspectives In the years to come, the development of RECIFS will focus on three domains: data, functionalities and integration. The current data available in the repository will be updated on an annual basis to provide updated records for the different environment variables. Furthermore, new variables that describe environmental factors shaping the dynamics of coral reef species will be added to the database as these variables become available and/or are required; we encourage coral reef researchers and conservation actors to contact us (the authors) to propose environmental descriptors to be added to RECIFS. With the future improvements of remote sensed data and environmental modelling techniques, we also aim to increase the spatial and temporal resolution of the reef cells, so that the environmental characterization can be more pertinent to fine-scale dynamics of the reef ecosystem  (Murray et al.
, 2020). Information on reef connectivity is necessary for undertaking effective conservation efforts, as it informs MPA managers on how to establish networks that best facilitate the larval dispersal to/from key conservation units (e. g. reefs hosting thermally adapted corals;  Matz et al. , 2020; McCook et al. , 2010). Going forwards, it will also be important to integrate RECIFS with other projects and complementary datasets that characterize alternative facets of reef environments. This could be done, for instance, by linking RECIFS with Reef Cover, which is a classification system based on remote sensing and in-situ data that categorizes reefs worldwide by habitat type (e. g. reef slope, reef crest, shallow lagoon, etc. ;  Kennedy et al. , 2021). Importantly, the cross-link between repositories should also be extended to other types of data (i.
, 2021; D'Angelo & Wiedenmann, 2014; De'ath & Fabricius, 2010; R. Jones et al. , 2020; McClanahan et al. , 2020; Sully et al. , 2019). In (C), we show the association between SDI and four of these variables: standard deviation of PH (PH_025_DS_sd), sea surface temperature (SST_050_DS_sd), degree heating week (DHW_010_WS_sd), and density of cropland (CROP_025_me). Based on the associations described above, the RECIFS data were used to predict SDI for each reef of the Caribbean (D). This spatial prediction highlighted regions expected to host reefs with high coral diversity, for instance in Belize (highlighted by the purple "I" on map); Bonaire, Aruba and Curaçao ("II"); Dominica and Martinique ("III"); and the Andros Island in Bahamas ("IV"). The mean absolute error (MAE) of the prediction was ~11% of the range of SDI measured in real surveys and did not appear to increase with distance from survey locations.
, 2017). Each sample in this dataset was genotyped at 17,007 single nucleotide polymorphisms (SNPs). Using genome scans, we identified 62 outlier SNPs that did not follow the neutral genetic structure of the population (i. e. SNPs potentially involved in local adaptation). We then ran a principal component analysis (PCA) to summarize the allelic frequencies of outlier SNPs into 12 axes of outlier genomic variation (outlier-PCs). Using the analytical pipeline shown in (B), we investigated the association between the spatial distribution of outlier-PCs and 302 variables from RECIFS, characterizing the reef environment for specific spatial and temporal scales. We ran a redundancy analysis (RDA; C) and found that part of the outlier-PCs variation (R 2 =0. 52) could be explained by two environmental variables.
As for outlier-PC9, we assumed that low values corresponded to an adaptation to high variability in Phosphate concentration and observed reef cells with such values in the Shark Bay area and the Kimberly region ("II" on map). These predictions must interpreted with care, as the mean absolute error (MAE) was non-negligible (~36% and 14% of the range of outlier-PC1 and outlier-PC9, respectively) and increased with distance from sampling locations. Yet, these results suggest that reefs of the Shark Bay area might host a stripey snapper population with exceptional adaptive capacities to both thermal stress and variability in phosphate concentration, compared with the reefs in the northeastern regions. In the original work that produced the genomic dataset used in this case study, DiBattista and colleagues highlighted a connectivity break between the reefs in Shark Bay and those located in northern areas  (DiBattista et al.
Figure1. Data workflow. Raw environmental data used in RECIFS comes from publicly available repositories, and have distinct temporal resolutions (monthly, annual or no resolution). Shapes of coral reefs worldwide were summarized into a regular grid (reef cell size: 5-by-5 km), and raw environmental values were extracted for each reef cell. These extractions were performed using four different buffer sizes (2. 5 km -on reef, 10 km, 25 km or 50 km) around the centers of reef cells. The result is a set of tables ("Extended tables") describing variation across reef cells for each environmental variable, at different buffer sizes and through different time periods (months or years). These tables are synthesized into a "Summary table", reporting overall temporal average and standard deviation of each environmental variable, at every reef cell.
Datasets included in RECIFS. For each dataset, the table shows the name and description of the variable of interest, the temporal window covered by the dataset (with the temporal resolution in parenthesis), the spatial resolution of the dataset, the source repository of the dataset (with the abbreviations explicited in the bottom) and the identifier of the original product from the source repository. Marine Service, 2022). This dataset is obtained from a stack of raster images displaying global monthly averages of eastward and northward sea surface velocity, from 1993 to 2019. Using custom R-scripts featuring the raster and the circular(v. 0. 4; Agostinelli & Lund, 2017).
, 2015, 2018; Hsu et al. , 2019) SEDAC Socioeconomic Data and Applications Center, Columbia University (Center for International Earth Science Information Network, 2022) GMRT Global Multi-Resolution Topography Data Synthesis (Ryan et al. , 2009) Data structure: surface currentsThe raw environmental dataset used to characterize sea currents was sourced from the Copernicus Marine Service (dataset id: GLOBAL_REANALYSIS_PHY_001_030; EU Copernicus.
Quantitative estimates of urban emission reductions due to COVID-19 restrictions based on atmospheric measurements have so far only been estimated for a few cities worldwide  (Gualtieri et al. , 2020; Lamprecht et al. , 2020; Sugawara et al. , 2021; Velasco, 2021; Yadav et al. , 2021). In this study we present CO 2 fluxes measured by a network of 13 EC stations in 11 European cities. These datasets span several years before the pandemic until October 2020. Urban EC stations operating before and during 2020 present a unique opportunity to investigate how the drastic perturbations in human activity caused by the COVID-19 pandemic have impacted local CO 2 emissions. CO 2 flux data at halfhourly resolution allows for temporal changes in CO 2 emissions to be tracked both during the initial lockdown period and during the subsequent recovery phase when economic activities and mobility gradually resumed.
Data were not gap-filled as it was not required for the type of analysis performed. Flux observations from previous years up to and including 2019 were used as a reference to compare changes before, during, and after the lockdown periods with related restrictions that affected each city district. Pre-2020 records span between 2 and 14 years depending on the station (Table  S1 ). We focused our analysis on four distinct periods defined following the Oxford COVID-19 Government Response Tracker (OxCGRT) Stringency Index (SI)  (Hale et al. , 2020a (Hale et al. , , 2020b)). The SI quantifies policies that governments have taken to respond to the pandemic; it is calculated as the average of a set of macro-indicators of containment and closure policies including closure of schools, universities and workplaces, cancelling of public events, limits on private gatherings, shutting-down public transport, orders to "shelter-in-place" or otherwise confine to homes, restrictions on internal movement between cities/regions, and restrictions on international travel.