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5,061 results for “access”
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Creating multi-themed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method
This dataset was created for the following publication: Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. Under review. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Submitted to Ecology and Evolution July 2016. This dataset includes lake total phosphorus (TP) and Secchi data from summer, epilimnetic water samples, as well as 52 geographic variables at the HU-12 scale; it is a subset of the larger LAGOS-NE database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS-NE compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.054.1 for lake water chemistry data and LAGOSGEO version 1.03 for geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. The dataset is a subset of the following integrated databases: LAGOSLIMNO v.1.054.1 and LAGOSGEO v.1.03. For full documentation of these databases, please see the publication below: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4 .
Accessible Oceans: Auditory Display. Ocean Response to Extratropical Storm Hermine
<p>The twenty-one tracks make up an auditory display of the ocean response to extratropical storm Hermine in 2016. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/VGWHypmrFPZ1NqzeqZuM">listen online here</a>.</p> <p>The ocean data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the <a href="https://datalab.marine.rutgers.edu/ooi-nuggets/extratropical-storm-hermine/">OOI Nugget</a> developed by Dr. Leslie Smith. Please note that the Ocean Labs data nugget does not include sea wave height as part of its graph that we sonified. Storms also impact sea wave height, and Dr. Leslie Smith acquired this data from the OOI so that we could include it in the sonification and auditory display.</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
The DataCons Project: An Open-Access Archive of Late Roman Consular Dates
<p>The DataCons Project offers an open-access dataset of late Roman consular dating formulae from CE 284 to 541. Aimed at aggregating consular materials discovered globally, presently it contains over 4,800 documents penned in three distinct scripts, originating from ten regions of the late Roman world and categorised by material type and textual content.</p><p>With its roots in prominent scholarly references, every entry undergoes rigorous verification, including palaeographical assessments and exact transcription of dating formulae. Distinct columns highlight potential dating, the author's selected date, and further specificity, ensuring the dataset's precision. Its evolution promises broader temporal coverage, and its structure facilitates ease of use and extensive potential for interdisciplinary research.</p><p>The current version of the dataset (2.0.0) presents the Latin and Greek documentation dated CE 476 to 526, exclusively comprising papyri and inscriptions. It is anticipated that there will be periodic updates and an upcoming release of an online database titled <i>DataCons: The Digital Database of Late Roman Consular Dates</i>. This will enhance and support research utilising the DataCons dataset.</p>
Survey Data on Current Open Access Terms and Future Trends (2024)
<p><strong>Description:</strong><br>This dataset contains the analysis, codebook, and raw survey data from the 2024 survey <em>"Open Access – Current Terms and Future Areas of Focus"</em>. The survey aimed to gather perspectives from Open Access experts in the German-speaking region, focusing on the evaluation of current Open Access terminology, concepts, and emerging trends.</p> <p>The survey highlights how Open Access terminology has evolved over the past two decades and explores current perceptions regarding key terms in the Open Access discourse, as well as the anticipated future developments in this field. A total of 131 complete responses (<em>N=131</em>) were collected, providing valuable insights into the views of professionals working in Open Access publishing, information infrastructures, and scientific publishing houses.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>codebook_oa_2024_2024-11-21.xlsx</strong>: The codebook, including detailed explanations of the variables, codes, and definitions used in the survey.</li> <li><strong>survey_results_oa_2024_2024-11-21.xlsx</strong>: Anonymized raw data from the survey, including both quantitative and qualitative responses from the participants.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: CSV file containing the key terms and concepts identified by participants in response to the question on Open Access terminology.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: An additional CSV file with detailed classification and analysis of the terms related to Open Access, including their frequency and significance based on participant responses.</li> </ol> <p><strong>Methodology:</strong><br>The survey was conducted via an online questionnaire distributed from September 7 to October 15, 2024, to professionals working in Open Access, both within information infrastructures (e.g., libraries) and in academic publishing houses. The survey gathered both qualitative and quantitative data, focusing on how Open Access terminology is understood and its future developments. The data were cleaned, anonymized, and analyzed using appropriate statistical and content analysis methods.</p> <p><strong>Purpose and Use:</strong><br>This dataset is valuable for researchers and professionals studying Open Access terminology, trends, and future developments. It provides insights into the current understanding of Open Access within the academic community and can be used for comparative studies, policy analysis, and future Open Access research.</p>
GESIS - Leibniz Institute for the Social Sciences data access categories
<p>Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.</p> <p>The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.</p>
Data of European University Association (EUA) Open Access Survey 2017-2018
<p>This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>
IPBES Data Management Tutorials - Session 3.6: Data management report details: Data sharing and access considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management c</em>hapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session <em>Data sharing and access considerations </em>covers details on licenses, exceptions to data sharing, and intellectual property considerations. </p>
IPBES Data Management Tutorials - Session 5.3: Literature access tools
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on literature access tools introduces Research4Life, a tool which provides experts in middle to low income countries access to scientific and grey literature.</p>
Global Naturalized Alien Flora (GloNAF). Open access data to support research on understanding global plant invasions.
<p>This dataset is a snapshot of the Global Naturalized Alien Flora (GloNAF) database, version 2.02. GloNAF is a continuously updated, curated compilation of alien naturalized vascular plant inventories for geographic regions from around the world. The dataset has 16,429 unique taxa reported as naturalized or invasive and covers 1,343 regions (including 427 islands) from 336 data sources. For each region, the status (invasive, naturalized) is provided as listed in the original source. We provide the scientific names included with the original data source, and the matching accepted name or synonym of the taxon as given in the World Checklist of Vascular Plants (WCVP) Version 12. In addition, we provide an ESRI shapefile of polygons for each region. We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources.</p> <p>The 'glonaf_flora2.csv' file lists the IDs ('taxon_wcvp_id') of all naturalized taxa contained in GloNAF and the regions they occur in. The 'glonaf_taxon_wcvp.csv' lists the original taxon names provided in the source data along with the corresponding accepted taxon name from the WCVP (version 12) for all alien taxa in GloNAF, regardless of their naturalization status. To link taxon names with naturalization records, join the 'id' column of the 'glonaf_taxon_wcvp.csv' file to the 'taxon_wcvp_id' column in 'glonaf_flora2.csv' . Additional information regarding the original source of the data ('glonaf_reference.csv'), specific attributes of the taxon lists ('glonaf_list.csv') and the region ('glonaf_region.csv') can also be joined similarly to 'glonaf_flora2.csv '. </p> <p> </p>
Accessibility Indicators to services at EU scale - 1km grid indicators
<p>This archive makes available <strong>accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale</strong> (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses <strong>only open source and free routing engines (OSRM)</strong>, based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.</p> <p>The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a <a href="https://ec.europa.eu/regional_policy/information-sources/maps/urban-centres-towns_en">common methodology</a> for all Europe. Within GRANULAR activities, we consider the towns and cities layer as <strong>a proxy</strong> to discuss on little and medium commercial centralities in Europe.</p> <p>This methodological framework, <strong>implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks</strong>, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.</p> <p>Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) <strong>describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells.</strong></p> <p>All the output data is also available. </p> <p> </p>
Public sequence accessions from INSDC, COG-UK and CNCB and EPI_SET from GISAID for SARS-CoV-2 genome sequences in 2023-08-01 UShER tree
<p>Genome sequences and metadata for the accessions in the .tsv.gz (gzip-compressed tab-separated text) files are freely available from their corresponding sources:</p><ul><li>insdc.accessionNameDate.tsv.gz: INSDC (GenBank, ENA, DDBJ) sequences and metadata may be downloaded using NCBI Datasets: https://www.ncbi.nlm.nih.gov/datasets/taxonomy/2697049/ (7,361,734 accessions used on 2023-08-01)</li><li>cog.accessionNameDate.tsv.gz: COG-UK sequences and metadata may be downloaded from https://cog-uk.s3.climb.ac.uk/phylogenetics/latest (as of publication); most COG-UK sequences have been submitted to ENA and are available from INSDC/NCBI Datasets as well. (724,978 accessions used on 2023-08-01)</li><li>cncb.accessionNameDate.tsv.gz: Sequences and metadata from several databases at the China National Center for Bioinformation (CNCB) may be downloaded from GenBase: https://ngdc.cncb.ac.cn/genbase/ (26,604 accessions used on 2023-08-01)</li></ul><p>GISAID data are subject to restrictions on sharing described in https://gisaid.org/terms-of-use/. Genome sequences and metadata are available to registered GISAID users as part of EPI_SET_231106ax at https://doi.org/10.55876/gis8.231106ax (7,718,061 accessions used on 2023-08-01).</p>
Open Research Skills Workshops - Open access publishing Workshop
<p><strong>This is the first workshop on Open Access Publishing in a series of workshops about Open Research Skills.</strong></p><p>This workshop covers:</p><p>Introduction to open access publishing</p><ul><li>Types of open access publishing</li><li>Examples of open access publishing journals and platforms</li><li>Benefits of open access publishing</li><li>Types of outputs that can be published</li></ul><p>Demonstration </p><ul><li>Demonstrating open publishing </li><li>Showing how a reproducible article is published and all the different outputs that are linked to it and how to do this</li></ul><p>Exercise</p><ul><li>Discuss and explore open publishing giving examples of different articles that show how open publishing works. We will pick those that show data and code deposited in repositories and also that use of protocol.io for publishing open methods</li></ul><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li><strong>24th February 2023 - Open access publishing</strong></li><li>24th March 2023 - Using repositories</li><li>21st April 2023 - GitHub basics</li><li>28th April 2023 - GitHub collaborative workflows</li><li>26th May 2023 - Standard vocabularies and ontologies</li><li>30th June 2023 - FAIR data</li></ul><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages.</p>
Survey Data and Analysis on Open Access Strategies (2022)
<p><strong>Description:</strong><br>This dataset includes the analysis, codebook, and raw survey data from a 2022 survey titled <em>"Which Open Access Strategies Are Relevant?"</em>. The survey targeted professionals in library and information sciences specializing in open access and scholarly publishing.</p> <p>The dataset is based on 100 adjusted responses (<em>N=100</em>) and aims to provide insights into the strategies and challenges associated with open access implementation in academic and professional environments.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>analysis_oa-strategies-2024-01-14.xlsx</strong>: Processed data and key analyses, including summary tables and graphs.</li> <li><strong>codebook_oa-strategies_2024-01-14.xlsx</strong>: Comprehensive documentation of variables, codes, and their definitions for interpretation of the raw data.</li> <li><strong>survey_results_oa-strategies_2024-01-14.xlsx</strong>: Anonymized raw data from the survey, suitable for further analysis.</li> </ol> <p><strong>Methodology:</strong><br>The survey employed a structured questionnaire distributed in 2022 to professionals in library and information sciences. It focused on identifying key strategies, institutional policies, and perceived barriers to open access. The collected data were cleaned and anonymized to ensure privacy and compliance with ethical standards.</p> <p><strong>Purpose and Use:</strong><br>This dataset is designed for researchers, policymakers, and information science professionals. It is particularly valuable for studying open access adoption strategies, evaluating institutional policies, and conducting comparative research.</p>
Access Network of Henri III of France's apartment according to his 1585 court ordinances
<p>This dataset collects the stipulations of access for courtiers in the royal apartment as described in Henri III of France's 1585 court ordinance (Paris: Archives Nationales, KK 544, fol. 55r-141r).</p> <p>The dataset was created with the aim to establish the degree of accessibility of both the (individual) spaces in the royal apartmant as well as the king himself and analyse in which ways king Henri III of France managed to balance his need for privacy with the courtiers' expectations of access. The results are to be / were published in <em>Current Research in Digital History</em>. </p>
FASTA file containing to the MYB encoding gene Ant1 genomic sequences corresponding to wild and cultivated tomato accessions
<p>Fasta sequence correspond to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>).</p>
FASTA file containing the MYB encoding gene An2-like genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequence corresponds to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), <em>S. lycopersicum </em>variety Indigo Rose (Yan et al., 2020), <em>S. lycopersicum</em> accession LA1996 [MN242011.1 (Colanero et al., 2020)], <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI)(available at NCBI: <a href="https://www.ncbi.nlm.nih.gov">https://www.ncbi.nlm.nih.gov</a>).</p>
FASTA file containing the MYB encoding genes at the Aft locus with genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequences correspond to the MYB encoding genes <em>An2-like </em>and <em>Ant1</em>. The genomic sequences were combined correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), LA1996 [MN242011.1, EF433417.1(Sapir et al., 2008; Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI) (available at NCBI: <a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov</a>).</p>
Data access for figures of Chen, Ginoux, Wyart, Mora & Walczak
<p>README: </p> <p><br> Pandas DataFrame</p> <p>to load: <br> import pickle<br> pickle_filename = 'YOUR_DATA_PATH/df_name.pkl' # change accordingly<br> with open(pickle_filename, 'rb') as pickle_in:<br> df_name = pickle.load(pickle_in)</p> <p><br> Motorneuron data:<br> Fish 3 Trial 1 and Fish 5 Trial 2 for Figure 3.<br> Fish 5 Trial 2 for figure 4.</p> <p>Columns:<br> - Fish: fish index<br> - Trial: trial index<br> - fluo: fluorescence traces [n_cells x n_timesteps]<br> - fluo_type: 'dff' or 'f_smooth', respectively before and after smoothing procedure<br> - n_cells: number of cells in the plane (only those kept for analysis, "bad" cells removed)<br> - mid: middle cell, to split left vs right neurons (left until index mid-1, right from index mid and on)<br> - cell_centers: x and y position of the cell center [n_cells x 2]<br> - multivariate: boolean to indicate bivariate (False) or multivariate (True) GC<br> - GC: Granger causality matrix results [n_cells x n_cells]<br> - GC_sig: Granger causality matrix results, significant with original threshold (where Fstat > threshold_F) [n_cells x n_cells]<br> - GC_sig_new_thresh: Granger causality matrix results, significant with new threshold (where Fstat > new_threshold_F) [n_cells x n_cells]<br> - Fstat: F-statistics matrix [n_cells x n_cells]<br> - threshold_F: original threshold for the F-statistics significance<br> - new_threshold_F: new threshold for the F-statistics after the whole pipeline is applied</p> <p><br> Hindbrain data<br> Fish 6 Trial 07</p> <p>Columns:<br> - fluo: fluorescence traces [n_cells x n_timesteps]<br> - cell_centers: x and y position of the cell center [n_cells x 2]<br> - background: plane background for plotting [249 x 512]<br> - n_cells: number of cells in the plane<br> - tail_angle: array of angle of the tail [75000,] - 75000 timesteps: higher frequency than calcium imaging recording<br> - tail_angle_regressor: tail angle convolved to calcium decay function [75000,] <br> - is_swim: boolean array whether each cell in correlated to swim activity (True if pearson correlation between cell's fluorescence trace and tail_angle_regressor > 0.6) [n_cells,]<br> - swim_neurons: indices of swim-correlated neurons [n_swim_cells,]<br> - medial_neurons: indices of swim-correlated neurons [n_medial_cells,]<br> - SNR: signal-to-noise ratio for each cell [n_cells,]</p> <p>- BV_GC_medial: original bivariate (BV) Granger causality results matrix [n_medial_cells,n_medial_cells]<br> - BV_Fstat_medial: original BV F-statistics matrix [n_medial_cells,n_medial_cells]<br> - BV_threshold_F_ori: original threshold for the BV F-statistics significance<br> - BV_threshold_F_new_mat_medial: new threshold customized for each pair of neurons (BV) [n_medial_cells,n_medial_cells]<br> - BV_Fstat_normalized_medial: new BV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - BV_GC_normalized_medial: new BV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells]</p> <p>- MV_GC_medial: original multivariate (MV) Granger causality results matrix [n_medial_cells,n_medial_cells]<br> - MV_Fstat_medial: original MV F-statistics matrix [n_medial_cells,n_medial_cells]<br> - MV_threshold_F_ori_medial: original threshold for the MV F-statistics significance<br> - MV_threshold_F_new_mat_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - MV_Fstat_normalized_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells]<br> - MV_GC_normalized_medial: new MV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells]</p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.