dataesr/scientific-paragraphs-categorization huggingface dataset:
The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach, and its future system integration plans. These open data are available at https://github. com/sonyc-project/seeing-sound-dataset. -In Paris, the Rumeur network was created by the regional environmental agency Bruitparif together with the French Ministry of Environmental Health to monitor noise in the Île-de-France region. The network consists of long-term noise monitoring stations at fixed locations for multi-year longitudinal noise studies. Focusing on certain locations provides insight into how nighttime noise may impair the sleep length of inhabitants of such cities in the proximity of the CDG airport as Saint Prix (Figure  3 :.
Examples of light pollution-pertinent open data sets Multiple data sets propose the mapping of light pollution, which may be relevant for researchers aiming to pollution may differ from one country to another (see for example India in its regional environment. -The Globe at Night program is another international citizen science campaign aimed at raising public awareness of the impact of light pollution, by inviting citizen scientists to measure their night sky brightness and submit their observations via computer or smart phone. Light pollution not only threatens our "right to starlight", but can also affect energy consumption, wildlife, and health. More than 100,000 measurements have been contributed from people in 115 countries during the winter and spring campaigns over the last nine years, making the Globe at Night program the most successful light pollution awareness campaign to date.
All data are available for analysis at https://www. datastro. eu/explore/dataset/imageserver/map/?disjunctive. limitingmag&disjunctive. cloudcover&disjunctive. constellation&disjunctive. country&location=2,0,-0. 2725&basemap=mapbox. light. -In principle, scientific data sets from satellites may be available for free as part of educational programs of the European Space Agency (ESA) or the National Aeronautics and Space Administration (NASA), but they are not easily accessible by the public. However, a new web application called "Radiance Light Trends" has made it possible since 2012 to quickly select a region or a site and analyze the trends in light emissions observed by satellites since 1992. This is a GEOEssential project funded by the EU's Horizon 2020 program (GEOEssential project: http://www.
This is why the European community encouraged a specific and interactive report on this question, as well as the development of open-source data sets that are accessible by students and researchers at http://data. europa. eu/euodp/en/data/dataset/S843_73_3_EBS347. Another resource relevant to open data is the INTERMAGNET data set, available at http://www. intermagnet. org/data-donnee/download-eng. php#view. This data set is operated according to principles and conditions that are accepted as being necessary and desirable for maintaining rapid magnetic observatory data exchanges between magnetic observatories for the international scientific community and commercial users. It also encourages the establishment and maintenance of digital observatories in developing countries, with the involvement and enhancement of local science and technology.
Importantly, one of the main factors the authors found to explain this high rate of sleep disorders was the long commuting time for inhabitants, which severely influenced their TST. Conclusions-Limitations. Our aim here has been to show that even if the sleep and wake rhythms of humans share a strong interdependency with their environment, this environment has drastically changed in the recent past, However, the openness and use of free data, is not a universal possibility, as there are several rules or license agreement for openness and use of free data. Many datasets needs specific and paid access that are limiting their availability. Moreover, it is not easy to combine data from environment with behavioral or human datasets, with potential bias on the signification of data, the reliability of the sources, the period of sampling, the quality of datasets.
Europe by night on July 30, 2019). New satellite data are automatically entered into the database and taken into account in the calculation. This is particularly useful for interested citizens, environmentalists, or journalists, as well as public health and municipal administrations. The app might also be useful in analyzing natural hazards and the planning or rebuilding of infrastructure. The data underlying the app comes from two satellite instruments run by various government agencies in the USA over the last several decades. The data from 1992 to 2013 comes from the Operational Linescan System of the Defense Meteorological Satellite Program (DMSP) satellites. Since 2012, the source of data is the Day/Night Band of the Visible Infrared Imaging Radiometer Suite instrument (VIIRS DNB). There are several options to choose from for displaying the satellite data in "Radiance Light Trends".
Neuroimaging data Neuroimaging data were acquired using 3T MRI scans (Siemens Prisma, Germany) with a 64channel head coil. Multimodal MRI protocol was use with the sequences as followed: i) T1-weighted images (MP2RAGE; TR = 5s, TI = 700/2500ms, fov = 256, 1mm isotropic, Ipat acceleration of 3) were first denoised and pre-processed (segmentation, normalization to Montreal Neurological Institute space and smoothing at 10mm) using the Computational Anatomy Toolbox (www. neuro. uni-jena. de/cat/) extension from SPM12 (www. fil. ion. ucl. ac. uk/spm/). ii) A multi-echo echo-planar imaging (EPI) sequence was performed for the resting states, with a multi-slice, multi-echo acquisition scheme (TR = 1. 9s, TE = 17/36/56ms, Ipat acceleration factor 2, Multi-band 2, isotropic voxel size 3mm) and were processed with the MEICA toolbox (www.
github. com/ME-ICA/me-ica/). This toolbox implements standard pre-processing steps: slice timing correction and realignment to the first volume driven by the first echo, and coregistration to the anatomic volume. A single warp was applied to combine realignment and coregistration. Then, we performed a principal component analysis to reduce the dimensionality of the dataset by removing thermal noise, and an independent component analysis decomposition to separate BOLD (blood-oxygen-level dependent) from non-BOLD components based on the echo time dependence of the BOLD component. After the independent component analysis, we obtained a dataset where both thermal noise and physiological noise such as movements, respiration artefacts, and cardiac artefacts were removed. Finally, we normalized the dataset using the flow field generated by the T1 processing from CAT12.
iii) A multi-shell diffusion tensor imaging (DTI) acquisition was also performed (TR = 3. 5s, TE = 75ms, Multi-band of 3, isotropic voxel size 1. 75mm, 60 direction with b = 2000 s/mm2, 32 direction with b = 1000s/mm2 and 8 direction with b = 300s/mm2, one b = 0 was each 10 directions). Data pre-processing was performed using the FMRIB software library (FSL) pipeline to extract fractional anisotropy (FA) and mean diffusivity (MD), involving correction for motion and eddy currents, removal of non-brain tissue of each volumes, tensor reconstruction, non-linear registration, alignment to the MNI space and thresholding data at 0. 2.
Data Shortly thereafter, HCW (all hospital employees) with positive results were prospectively contacted by phone by the clinical research team and invited to participate to the study. After three unsuccessful attempts, the participant was considered unreachable. During a phone interview, the following data were collected on a standardized questionnaire: age, gender, profession, symptoms and date of onset, and exposure to SARS-CoV-2 within the 10 days preceding symptoms onset. The study was basically designed to assess sources of exposures of HCW and for confidentiality reasons, we did not collect precise data on past medical history and comorbidities. Exposures were classified as: in-hospital related to patients' care (average number of close contacts per day with COVID-19 patients with and without PPE, compliance to infection prevention and control [IPC] protocols), in-hospital related to other activities (contacts with colleagues during meal breaks, meetings etc.
A residual number of contaminations was reported as of April 1 st among HCW, mainly driven by contacts with undiagnosed patients or colleagues without protection. Conversely, children attending community childcare facilities seemed to play a minor role in residual transmissions to HCW that were observed during the second phase of the epidemic. Compared to previous studies in China  (1) , the US (5) and the UK (3,4) on SARS-CoV-2 infections in HCW, we did a systematic investigation among all staff members infected with COVID-19 and were able to confront an adult and a pediatric setting. Epidemic curve among HCW was also compared to hospitalization needs in the general population. Therefore, our data collected from the early beginning of the French epidemics give an unprecedentedly reported landscape of HCW infection with COVID-19.
Acknowledgments The authors warmly thank medical students involved in data collection:  Laurence Clastres ,  Mathilde Lehmann ,  Aline Pellegrini ,  Marine Sisouvan ,  Ilana Slotine ,  Diem Soubou  and  Abigaëlle Vergnet , the following physicians and nurses who actively contributed to the screening:  Claire Aguilar ,  Chantal Delmas ,  Claudine Duvivier ,  Muriel Fortier ,  Béatrice Grandordy ,  Marc Lecuit ,  Léonie Meyer ,  Gabrielle Paluszek ,  Guillemette Thin  and  Christine Vinter , as well as  Jeanne Marty  for providing hospital data and  Bruno Coignard  for his help on data interpretation.
In this study the notched noise stimuli were created using notch bandwidths of 0. 4, 0. 5, 0. 6, 0. 75, 0. 8, and 1 octave at center frequencies of 1,  000, 1,414, 2000, 2,828, 4,000, and 5,657 Hz, respectively. The choice of center frequencies and notch bandwidths is based on a study by  Leske et al. (2014). These stimuli were created in MATLAB and were all matched for overall RMS power. The stimuli were played binaurally at 72 dB SPL over stereo headphones.
| EEG data collection and preprocessing Continuous EEG data were collected from each participant in response to the three stimulus conditions played in the ERP paradigm. The data were collected using a 64 channel Neuroscan Synamps 2 Quick Cap configured per the International 10-20 placement system with a reference close to Cz using the Neuroscan Scan 4. 5 software. The impedance of each electrode was maintained at <5 kΩ. The data was sampled using the Neuroscan Synamps 2 amplifier at 500 Hz with F I G U R E 2 Summary of the ERP paradigm. A fixation cross is presented for 3 s followed by the stimulus for 3 s. A 1. 5 s silence period is provided to assess the perception of a ZT illusion. The rating of the percept is asked for following the completion of the silence period online band-pass filtering at 0.
1-0. 70 Hz. The stimuli were presented through the Neuroscan Stim II system which triggered the Scan 4. 5 software every time a stimulus was presented. Data were then preprocessed using MATLAB and EEGLAB. The preprocessing pipeline included removing of disconnected and unused channels, re-referencing to an average reference, and bandpass filtering using a zero-phase 3,115-point basic Hamming-windowed sinc FIR filter with the lower edge of the frequency pass band at 0. 53 Hz and higher edge of the frequency pass band at 55 Hz and a transition bandwidth of 0. 53 Hz. The resulting filter passband edges at the -6 dB cut-off was 0. 26 Hz and 55. 6 Hz. Data from the remaining electrodes after removal of disconnected and unused channels were then subjected to temporal independent component analysis (ICA) using an infomax algorithm  (Jung, Makeig, Bell, & Sejnowski, 1998) , which was used to remove muscle artifacts, eye blinks, saccades and other noise transients.
Statistical analysis fMRI data analysis Data were analyzed using SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) and Matlab R2019a (Mathworks, Inc. , Natick, MA). The first four volumes of each fMRI run were discarded due to the time needed to launch the IGT synchronized with fMRI acquisition. Then, the first 40 trials corresponded to the Ambiguous and the others to the Risky phase of the IGT. GE-EPI data were re-oriented to the anterior commissure, slice-time corrected, realigned to the first volume, coregistered, spatially normalized (based on T1) using the DARTEL algorithm, and smoothed with an 8-mm FWHM Gaussian filter. Contrast images were computed for risky vs. safe choices for each participant using a first-level general linear model. Realignment parameters were added in the regressor to remove specific activation of head movements and a high-pass filter (cut-off = 128 s) was used to remove non-physiological slow signal shifts.
A secondlevel analysis was then performed using a factorial design to evaluate the interaction between Group (SA, AC, HC) and Condition (Neutral, Concordant, Discordant) separately for the two IGT phases (Ambiguous and Risky). For the voxel wise analysis, significance threshold was set at p < 0. 001 (uncorrected), with k ≥ 10 voxels. Specific regions of interest (ROI) were chosen on the basis of an a priori hypothesis concerning the specific anatomical brain regions that could be associated with decision making, as done in Olié et al. 15. The ROIs were anatomically defined by Anatomical Automatic Labeling (AAL), using masks provided by the Wake Forest University PickAtlas software (http://fmri. wfubmc. edu). OFC was defined as the orbital parts of the inferior frontal gyrus, middle frontal gyrus and medial frontal gyrus (including the medial and lateral parts) (corresponding to the Brodmann areas (BA) 11/47), VLPFC as the opercular part of the inferior frontal gyrus (BA 44/45), MPFC as the medial part of the superior frontal gyrus (BA 10), and ACC (BA 24/32), and DPFC as the middle frontal gyrus (BA 8/9/46) (Supplementary Fig.
Correlations between fMRI and behavioral data The ROI beta values of each patient for the ambiguous and risky phases were extracted. Pearson's correlations between the beta values and the behavioral data (reaction time and IG index) showed significant negative correlations between the IG index (CD -AB) during the risky phase and the activation of all the brain areas (all p < 0. 05 FDR corrected). Thus, higher activation in prefrontal areas was associated with lower preference for the safe decks (lower IG index) during the risky phase (see Table  1  in supplementary material).
However, we cannot exclude bias in this study, as the participants who decided to answer the questions were already interested in the topic. For instance, the sample showed an over-representation of women as well as of people belonging to an animal protection association or who had a pet. Whilst this bias does not change our statistics about socio-demographic factors influencing the scores (Section 3. 3. ), it could influence the distribution of scores, both globally and per species (Section 3. 2. ). Moreover, the questionnaire was sent exclusively to French citizens, and we know that the cultures of humans influence their position regarding animals  [19, 20, 66]. It is therefore important to continue this study on other datasets. We also identified anthropomorphism as the main mechanism underlying the answers of participants, but anthropomorphism may be just one of a number of possible explanations.
Acknowledgments: We thank the participants who agreed to answer the study questionnaire. We also thank the scientists who accepted to give their expertise about the presence or absence of intentionality and beliefs in the behaviours shown by animals in the videos ( Yves Handrich ,  Sofia Bernstein ,  Julia Ostner ,  Carol Berman ,  Claudia Fichtel ,  Peter Kappeler ,  Andrew Whiten ,  Bonaventura Majolo ,  Marina Butoskaya ,  Erica van de Waal ,  Fany Brotcorne ,  Lauren Brent ,  Julia Fischer  and  Oliver Schuelke ).
EEG Recordings EEG signals were recorded using a 256-channel Sensor Net (from Electrical Geodesics) with a NetAmp 300 high impedance amplifier (Electrical Geodesics). Continuous recordings were performed with a high-pass set at 0. 1 Hz and a sampling rate of 1000 Hz; all channels were referenced to the vertex (Cz) and impedances were below 50 k. Cartool software (version 3. 7) was used to pre-process all EEG data, a notch filter fixed to 50 Hz was applied, data were band-pass filtered between 1 and 30 Hz, and an average re-referencing of all channels was performed.
Analysis and Statistics The neutral extracts were chosen to be neither pleasant nor unpleasant. Most of the participants reported difficulty distinguishing between the neutral and low pleasure emotional states and usually used only one of the two conditions when reporting low emotional state. These two conditions were therefore grouped together as the "Low pleasure" condition. We extracted the periods of time for which the participant reported each of low pleasure, high pleasure, or chills. Epochs of 1 s were extracted for these periods for the three emotional conditions: (1) low pleasure, (2) high pleasure, and (3) chills. Every epoch with visually detectable artifacts was removed from the analysis. A frequency analysis using fast Fourier transform (FFT) was performed on three frequency bands for each epoch of each condition, theta (4-8 Hz), alpha (8-12 Hz), and beta (12-20 Hz), using MATLAB software (2019a).
, 2001; Brunet et al. , 2011). Source localization was applied to five regions of interest, which were created using Cartool software and were based on fMRI literature  (Blood and Zatorre, 2001; Brown et al. , 2004; Salimpoor et al. , 2011) : (1) orbito-frontal gyrus (OFG), (2) supplementary motor area (SMA), (3) bilateral insula (Ins), (4) right superior temporal gyrus (RSTG), and (5) left superior temporal gyrus (LSTG). All statistical analysis was performed using R Studio software (version 3. 5. 2; 2018-12-20). For behavioral data, Pearson r correlations were performed to explore the link between the number of chills reported and BMRQ scores, sex, age, and years of musical practice. The normality of each data set was confirmed using Kolmogorov-Smirnov tests. For EEG analysis, three conditions (Low pleasure, High pleasure, and Chills) were compared for alpha frontal asymmetry, alpha prefrontal asymmetry, beta to alpha ratio, and for PSD values in the theta and alpha frequency bands for each ROI (LC, RC, LF, RF, LPF, RPF, LT, and RT) using repeated measures ANOVA (α = 0.
FUNDING This study was supported by a grant from the  University Hospital of Besançon  ( Appel à Projet Interne CHU -APICHU  no. API/2018/92  provided by the  Région Bourgogne Franche-Comté ) and benefited from financial support from the  Clinical and Integrative Laboratory of Neurosciences of Besançon  for device acquisitions. The sponsors played no role in the study.
As a conservation measure, several municipal, provincial and national nature reserves have been established, and artificial breeding has been encouraged by the government as a possible conservation measure. In this study, occurrence data were compiled from three resources: (1) the Global Biodiversity Information Facility (GBIF; http://www. data. gbif. org/) data set; (2) an open-source geodatabase that lists the nature reserves in China (https://www. osgeo. cn/data/); and (3) literature that surveyed and reviewed CGS distribution sites  (Wen 2015 , Turvey et al. 2018). The time span of these data covers the second half of the 20th century, when the CGS was widely distributed in China, to the early 21st century. After removing duplicate and apparently erroneous presence records (e. g. , records outside of China and terrestrial records far from water), 259 records remained, including records in 23 provincial and national reserves (Supporting Information Table  S1 ).
To diminish the effect of spatial autocorrelation originating from the spatial clustering of presence records (i. e. , more than one presence record in one environmental grid cell with a 30 arc-second spatial resolution, ca. 1. 0 km 2 at the equator), a spatial thinning or rarefying approach similar to that used by  Boria et al. (2014)  was applied to our presence dataset, selecting at most one record in each environmental grid cell  (Feng et al. 2019). Correspondingly, 253 presence records were finally retained. Since presence-absence algorithms are known to perform better than presence-only algorithms  (Elith et al. 2006 ) and true absence records for the CGS at a national scale are impossible to confidently identify, we instead generated 253 pseudo-absence records spanning the whole country and its adjacent areas using a randomization method conditioned by excluding pixels where presences are known  (Barbet-Massin et al.
Predictor variable selection The choice of predictor variables can have a strong effect on the quantification of the realized niche and therefore on SDM transferability in time and space  (Peterson et al. 2007 ). We initially selected nineteen climate variables and eleven non-climate variables considered ecologically meaningful for the spatial distribution of this species as candidate predictors (Table  1 ). The climate variables are commonly used bioclimatic variables (BIO1-BIO19) in the field of ecological niche modeling  (Hijmans et al. 2005 ) from the WorldClim database (http://www. world clim. org), including eleven temperature-related variables and eight precipitation-related variables. The eleven nonclimate variables were land environmental and human-related factors, including land elevation (LAE), waterbody area (WTA), flood hazard frequency (FHF), cropland area (CLA), development pressure index (DPI), human population density (HPD), human population count (HPC), human influence index (HII), human impervious area percentage (HIP), river fragmentation index (RFI), and dam density (DD).
Ensemble species distribution modeling and prediction Ensemble models are often considered superior to single algorithms because their combined framework can reduce the uncertainties of individual algorithms and provide more robust and reliable projections  (Grenouillet et al. 2011). We opted to use four different algorithms that have been frequently applied in the field of ecological niche modeling to establish the basal models required by the ensemble model. These algorithms were a generalized linear model (GLM), generalized boosting model (GBM), random forest (RF) model and multiple adaptive regression splines (MARS) model, and all these algorithms were fit using the default settings of the biomod2 package  (Thuiller et al. 2009 ) in the open-source statistical software R 3. 6. 1. A random 70% of the total presence and absence records were selected as the training set to calibrate the algorithms; the remaining 30% were withheld for evaluating algorithm performance.
This process was replicated 10 times to account for individual algorithm variabilities, avoid bias from the dataset split, and add rigor to the results. Model performance was assessed based on two metrics: the area under the receiver operating characteristic curve (AUC) and the true skill statistic (TSS). Basal models with TSS values over 0. 75 and AUC values over 0. 90 were selected to develop committee-averaged ensemble models and ensure that the developed ensemble model had the optimal predictive ability for occurrence probability  (Guisan et al. 2013 ). The ensemble models were used to predict habitat suitability for the CGS under current climate conditions and the independent and combined impacts of future climate and HPD changes. We generated projections for the current climate.