dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatiotemporal coverage of surveys. For each survey, we created in ArcGIS a shapefile of on-effort tracklines with associated latitude, longitude and date. We merged shapefiles of all survey tracklines and added them to a file geodatabase. We then intersected and spatially joined the tracklines to the 10 × 10 km grid of the study area. To examine the spatiotemporal coverage of surveys, we summed survey effort per grid cell in the entire study area and created subsets of surveys per month, year, and platform (ship or aircraft). Environmental covariates. We examined the environmental coverage of surveys with respect to four static covariates (depth, slope, distance to canyons, and distance to seamounts) and three dynamic covariates (sea surface temperature, primary productivity, and eddy kinetic energy) known to be important for cetaceans and likely to be included in future habitat-based density models (Supplementary Tables  S1  and S2 ).
We created rasters of static and dynamic covariates (data sources detailed in Supplementary Tables  S1  and S2 ) and resampled them to the 10 × 10 km grid of the study area (using nearest neighbour interpolation implemented in the Marine Geospatial Ecology Tools software  58  ) in ArcGIS. We used monthly contemporaneous and climatological resolutions for dynamic covariates  59. For surveyed grid cells (i. e. , representing the "available" dataset; see below), we used the month and year when the survey took place (contemporaneous resolution). For all grid cells of the study area (i. e. , representing the "prediction" dataset; see below), we used a monthly climatological average describing long-term oceanographic conditions in the Mediterranean Sea. Doing so allowed us to compare surveyed oceanographic conditions with long-term oceanographic averages (rather than oceanographic conditions of a particular year).
To obtain monthly climatological rasters, we averaged monthly rasters per grid cell from 1997-2016. These climatological means smooth out the inter-annual variability of oceanographic processes while maintaining the strong intra-annual variability which characterizes the Mediterranean Sea  52, 54, 55. Environmental coverage of surveys. To analyse the environmental coverage of surveys, we examined two multivariate environmental metrics: the extent of extrapolation versus interpolation and the proportion of prediction points near available data points. To evaluate the extent of extrapolation versus interpolation if models calibrated on the available survey data were used to predict cetacean densities in the entire Mediterranean Sea, we used the convex hull approach  37, 60, 61. The convex hull of a set of points is defined as the smallest convex set that contains these points.
Predictions inside the convex hull are interpolations while predictions outside the convex hull are extrapolations. The convex hull of a single covariate is the interval between the minimum and maximum data points (i. e. , a univariate environmental envelope). The convex hull of two covariates is a polygon with vertices at the extreme points of the data points (i. e. , a bivariate environmental envelope). A convex hull can be defined for any number of covariates, although visualization becomes difficult for more than two covariates and computational power starts to limit calculations for more than ten covariates  61. As stressed by Authier et al. (2017), the assessment of the convex hull does not require model fitting (only effort and environmental covariate data are required)  37. Here, environmental characteristics of the surveyed grid cells during the month and year of each survey represented the "available" dataset and monthly climatological environmental conditions of all grid cells of the study area represented the "prediction" dataset.
We considered the seven individual covariates enumerated above and the three following combinations of covariates: • all static covariates: depth, slope, distance to seamounts, and distance to canyons; • all dynamic covariates: sea surface temperature, primary productivity, and eddy kinetic energy; • all static and dynamic covariates: depth, slope, distance to seamounts, distance to canyons, sea surface temperature, primary productivity, and eddy kinetic energy. An examination of Spearman's rank correlation coefficients  62  showed that these covariates were not highly correlated (Spearman's rho <|0. 6|) in both available and prediction datasets. To further quantify extrapolation in environmental space, we calculated Gower's distances (G 2 ) for the three combinations of covariates listed above.
As for the convex hull, the calculation of Gower's distances does not require model fitting. The Gower's non-parametric distance between points i and j belonging to the available and prediction datasets, respectively, is defined as the average absolute distance between the values of these two points in each dimension, divided by the range of the data  [61] [62] [63]. With K environmental dimensions, the Gower's distance formula is as follows: ∑ = | -| = G K x x r 1 ij k K ik jk k 2 1 where r k is the difference between the largest and the smallest values of the available dataset for the k th covariate. Because of range standardization, covariates have equal contribution to the Gower's distance. We considered a prediction point to be in the neighbourhood of available data points if it was situated within a radius of one geometric mean Gower's distance of all pairs of available data points  61.
We stress that this definition of neighbourhood relates to the multivariate environmental space defined by the considered environmental covariates, not geographic space. The larger the proportion of prediction points near available data points in this multivariate environmental space ("proportion of data nearby" for brevity), the more reliable the extrapolation. We used the WhatIf package (version 1. 5-6)  64  in R (version R-3. 1. 1)  65 to calculate convex hulls and Gower's distances. We projected the extent of extrapolation versus interpolation and the proportion of data nearby to geographic space for mapping.
Acknowledgements This survey gap analysis would not have been possible without the vital contributions of many individuals and organisations. We are deeply indebted to our numerous colleagues involved in data collection and cleaning. Funding sources and specific acknowledgements pertaining to individual surveys are provided in references listed in Table 1. We thank  United States Fleet Forces Command and Naval Facilities Engineering Command Atlantic  for funding and support for the development of this gap analysis. We are grateful to  Megan C. Ferguson  and an anonymous reviewer for valuable comments on previous versions of this manuscript.
The Brain Imaging Data Structure (BIDS) project is a quickly evolving effort among the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. The first BIDS standard was proposed for the MRI/fMRI research community and has now been widely adopted. More recently a magnetoencephalography (MEG) data extension, BIDS-MEG, has been published. Here we present an extension to BIDS for electroencephalography (EEG) data, BIDS-EEG, along with tools and references to a series of public EEG datasets organized using this new standard.
Such diversity is an impediment to reuse of data and for building large scale EEG databases. The Brain Imaging Data Structure (BIDS,  Gorgolewski et al. , 2016 , RRID:SCR_016124) specification, originally proposed for magnetic resonance imaging data (MRI), is now a set of human brain research community standards used for organizing and sharing brain imaging study data within and between laboratories. BIDS therefore primarily addresses the issue of data structure heterogeneity. By agreeing on how to structure data using dedicated metadata files, dictionaries and naming conventions, BIDS standards foster interoperability and reuse of acquired datasets  (Wilkinson et al. , 2016). Because BIDS data are structured, BIDS also addresses issues of reproducibility and, when combined with sufficiently detailed annotation of experimental events and designs, allows creation and application of fully automated data analysis workflows applied to or across studies from many laboratories.
BIDS-EEG history BIDS-EEG was created following publication of the BIDS-MEG extension  (Niso et al. , 2018) , which itself derived from the specification originally dedicated to MRI/fMRI and behavioral data  (Gorgolewski et al. , 2016). Because MEG and EEG data share many features, the MEG organization was used as a template to ensure maximal compatibility between standards and to ease integration. Soon after the EEG extension was created, the intracranial EEG extension (BIDS-iEEG) was also developed  (Holdgraf et al. , in preparation). By developing the BIDS standard for MEG, EEG and iEEG in close collaboration, compatibility of relevant fields is ensured. As with the preceding BIDS standards, the creation and initial updates of BIDS-EEG have very much been a community effort driven by concrete user cases and open discussion.
BIDS-EEG Specification summary The specification follows the general BIDS: Each subject has a directory of raw data containing subdirectories for each session and modality. This is accompanied by a dataset_description. json file and a metadata file with the suffix _eeg. json, that specifies the task, the EEG system used (amplifier, hardware filter, cap, placement scheme, etc. ) 1. Optionally, a sourcedata directory containing the original behavioral and EEG data (if different format, see below) can be present, as well as a stimuli directory and a code directory to allow data conversion and preprocessing to be reproduced, as indicated in the original specification  (Gorgolewski et al. , 2016). Within each subject directory, the eeg subdirectory contains the EEG and metadata (see figure  2 ).
For instance, for a single session study, sub-XX would have subdirectory eeg which contains multiple EEG files as sub-XX_task-YY_run-ZZ_eeg. edf files corresponding the different runs of EEG data acquisition. In addition, sub-XX_task-YY_channels. tsv must be specified describing the parameters of the data acquisition and sub-XX_task-YY_electrodes. tsv and sub-XX_task-YY_coordsystem. json files should be specified, if the positions of the electrodes are known (see below). As for fMRI, sub-XX_task-YY_run-ZZ_events. tsv files should be present, indicating the onset of events, trial type, duration, responses, etc. As for fMRI, events. tsv files should be present, indicating the onset of events, trial type, duration, responses, etc. While such information is often present as one or several binary trigger channels in the EEG recordings, the representation of events is rarely explicit in the original data (e.
, 2016 ) has been integrated. The HED system allows to describe each event with a formatted string of pre-specified words that are called tags within the system. Tags are hierarchical and levels are delimited with a forward slash ("/"). Multiple tags can be combined, delimited with a comma. Parentheses (brackets) group tags and enable specification of multiple items and their attributes in a single HED string. Detailed event annotation is useful for electrophysiological data in which dynamics of brain activity are associated with multiple experimental events. The HED standard is an open community tagging schema whose toplevel tags are specified 2  but whose lower-level tags, giving more specialized detail about events in question, can be accumulated in personal, laboratory, or consortium libraries, or can be considered for publication as accepted tags and tag categories.
Data Formats EEG data can be saved in numerous formats and compared to MRI and MEG data there is a much larger heterogeneity of data formats. Allowing all existing data formats to be included as standard BIDS files would place a burden upon the potential users to deal with this diversity of the data, exacerbated by the fact that some data formats are proprietary or not openly documented. This problem might well undermine the positive effect that BIDS aims to have on the development of integrated apps and analysis software. The process of converging on a list of suitable data formats for BIDS-EEG was governed by three major requirements: A suitable data format should (i) address the needs of a large portion of the global EEG community, (ii) be a FAIR format  (Wilkinson et al. , 2016) , with a focus on interoperability, and should (iii) meet the technical requirements of neuroscientific workflows, such as saving numerical data with high precision.
As a solution to this challenge, the BIDS-EEG specification incorporates only two recommended official data formats: The European Data Format (EDF 3  ), which is an ongoing international effort to provide a common data format for electrophysiological recordings that started in 1992  (Kemp et al. , 1992; Kemp and Olivan, 2003) , and the BrainVision data format 4  , as used by Brain Products GmbH 5. While the BrainVision data format was designed by Brain Products GmbH for their proprietary EEG recording equipment and analysis software, it is based on the Microsoft Windows INI file and has a concise documentation 6. Both of these formats follow the three above mentioned requirements: (i) they are used widely in the community as indicated by a recent survey 7  , (ii) they have open access documentation, open source implementation for both reading and writing in at least two programming languages and are widely supported in multiple software packages (both open source and commercial) and (iii) have high numerical precision (16 and 32 bits respectively).
To accommodate a larger scientific audience and facilitate adoption, the BIDS-EEG standard also allows two unofficial commonly used data formats: the format used by the MATLAB toolbox EEGLAB  (Delorme and Makeig, 2004)  (. set and. fdt files), and the Biosemi format (. bdf 8  ). These formats, while not actively encouraged, are still included because of their popularity and their interoperability among the major software packages. Future versions of BIDS may extend the list of officially supported data formats, based on the fulfillment of the above mentioned criteria and usability to develop data analysis pipelines.
Electrodes versus Channels The notions of electrodes and channels are often used interchangeably in the context of EEG research. However for BIDS-EEG, it is crucial to distinguish between the two in order to provide an unambiguous documentation. An EEG electrode is attached to the skin, whereas a channel is the combination of the analog differential amplifier and analogto-digital converter that result in a potential (voltage) difference being stored in the EEG dataset. The reference and ground electrodes should not be referred to as channels and only as electrodes. Some systems (e. g. , Biosemi) have an active floating reference, whilst for most of the other systems, the potential at electrodes is neither amplified nor recorded. For BIDS-EEG, researchers must specify a channels. tsv and may in addition specify an electrodes.
Public EEG-BIDS datasets During the preparation of the BIDS-EEG specification, three full data sets have been made publicly available and study examples (with zero-byte datafiles) are available on the BIDS GitHub repository 9. Single session per participant (eeg_matchingpennies,  Appelhoff et al. , 2018)  The matching pennies dataset was collected as part of a student project to replicate a brain computer interface study of motor intention decoding. In this study, participants were playing a game of matching pennies against the computer. After initiating a countdown of three seconds, the participants raised either their left hand or their right hand; at the same time the computer presented a stimulus to the left side or to the right side. Participants could win against the computer by performing actions opposite to that of the computer.
The computer however had access to the real time EEG data and could make use of the participants' EEG activity before the end of the countdown, thus trying to decode a lateralized readiness potential and increasing the chances to win against the participant. In the dataset, the offline data of 7 participants are provided. Multiple sessions per participant (eeg_rishikesh,  Brandmeyer and Delorme, 2018)  This study was conducted at the Meditation Research Institute (MRI) in Rishikesh, India. All participants (25 meditators from the Himalayan Yoga tradition) were asked to meditate continuously throughout the experiment in their usual seated meditation position. Experience sampling probe questions were presented at random intervals ranging from 30 seconds to 90 seconds throughout the duration of the experiment.
Data format validation software As part of the BIDS project, datasets formatted to follow the BIDS-EEG standard can be validated using bids-validator, a JavaScript application that can be run locally using Node. js. Both a command line version 10  , and a browser version 11  are available. Note that the validator runs in the browser and no data is uploaded when using either version of the bids-validator preventing any data protection issues to arise. The functionality of the previous BIDS validator was extended to capture the specifics of EEG data. With this validation tool, scientists are empowered to check their newly formatted datasets and make full use of the data structure's strengths such as checking for missing data or underspecified metadata.
Data conversion software To help EEG practitioners to convert data from one format to EDF (16 bit) or BrainVision data format (32 bit), we have started a project to make data readers accessible that only depend on Python for both EDF 12  and BrainVision data format 13. This tool will be extended with time to provide a universal converter to prepare data for BIDS, as does for example dcm2niix for MRI 14. Data conversion utilities from many EEG formats to the EDF and BrainVision data format are available in MATLAB from the FieldTrip Toolbox 15. Community tools and software support EEGLAB  16 EEGLAB  (Delorme and Makeig, 2004 ) is a freely available, readily extensible open source software environment running on MATLAB (The Mathworks, Inc. ) for analysis of neuroelectromagnetic data, particularly EEG.
Fieldtrip 17 FieldTrip  (Oostenveld et al. , 2011)  is an open source MATLAB toolbox for channel and source-level MEG, EEG and iEEG analysis. It includes a collection of highlevel, consistent, well-documented and user-friendly functions that researchers combine in pipelines (MATLAB scripts) for their analysis. FieldTrip supports reading from most EEG formats, including the ones used in BIDS, and can also export to the EDF and BrainVision data formats. Analyzing data that is organized in BIDS is not very different from other data, except that researchers can read additional metadata (events and annotations) from the events. tsv sidecar file. FieldTrip includes the data2bids. m function to help users to organize their EEG, MEG, iEEG and MRI data in BIDS and to provide proper metadata annotation.
MNE 19 MNE  (Gramfort et al. , 2014 ) is a software suite for exploring, visualizing, and analyzing human electrophysiological data. It consists of three fully integrated core subpackages to be used with the Python programming language  (Gramfort et al. , 2013) , MATLAB and C/C++. Functions to read EEG data formats as supported by BIDS are provided. Furthermore, there is active development underway to make MNE-Python completely compatible with the BIDS specification in the form of the MNE-BIDS project 20. At the current state of the project, users can read their raw data using MNE-Python and then automatically format an initial BIDS directory with metadata extracted from the raw data already partially filled in the correct format. An example is available 21  ).
SPM 22 SPM  (Flandin and Friston, 2008 ) is a free and open source software for the analysis of neuroimaging data. It is mostly written in MATLAB and offers a wide range of methods for the analysis of PET, MRI, fMRI, EEG and MEG data  (Litvak et al. , 2011). The latest release, SPM12, includes a library, spm_BIDS. m, to interact with BIDS datasets. It has been extended to support the present EEG specification 23  and is also available as a standalone library 24.
Beyond sharing raw data This article describes the new EEG extension for the Brain Imaging Data Structure, and has limited itself to sharing raw data using previously developed community standards. Challenges that are specific to EEG, such as support for data formats, are still actively debated, and some additional formats will likely be incorporated once the technical issues and standards of FAIRness are achieved. The development of BIDS for EEG derivatives is also already underway (see BIDS Extension Proposal 21), which will allow sharing preprocessed data (see e. g. ,  Bellec et al. , 2017) , thus fostering re-analyses, meta-analyses, and new analyses without the burden of data preparation. • S. A. : critical review of the specification, moderating community interactions during the process, preparation of datasets and examples, coding of the bids-validator extension, coding of bids MNE tools, writing the manuscript.
• G. F. : critical review of the specification, coding of BIDS SPM tools, critical review and final approval of the version submitted. • C. P. : critical review of the specification, preparation of examples, critical review and final approval of the version submitted. • A. D. : critical review of the specification, preparation of datasets and examples, coding of BIDS EEGLAB tools, critical review and final approval of the version submitted. • R. O. : conception and design of the specification, moderating community interactions during the process, preparation of datasets and examples, coding of the bids-validator extension, coding of BIDS FieldTrip tools, critical review and final approval of the version submitted.
Acknowledgements We thank the members of the  BIDS community  for sharing their insights, stimulating discussions and helpful comments on early drafts of the EEG extension. We are grateful to  Dora Hermes  and  Chris Holdgraf  for sharing their article drafts of the iEEG specification and to  Scott Makeig  for discussions on data formats and metadata exchange. This work was supported by the  EU-H2020   Marie Curie "ChildBrain" Innovative Training Network  grant no. 641652  to  Robert Oostenveld.
Effect of the extraction procedure on the composition of the Epoisses virome Saint-Nectaire exhibited the lowest nanoparticles per cell ratio and Camembert the highest, Epoisses being an intermediate situation (Table  1 ). Furthermore, plate counting indicated a higher bacterial density in Epoisses cheese than in Camembert (Table  S1 ), bacteria being expected as the main targets of microbial viruses (bacteriophages or phages) in the cheese ecosystem. For those two reasons, we selected Epoisses cheese as the best candidate for further virome analysis. DNA was successfully extracted from VLP solutions produced from Epoisses cheese using the four procedures and prior the gradient step. DNA yields are available in Table  S2. DNA samples were then sequenced, in order to assess the impact of both filtration and chloroform treatment on the final sequence dataset (see Material and Methods section).