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Fig. 2 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 2. Infestation and larval density of pepper weevil in 2017. Vertical bars are standard errors of the means.
Fig. 5 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 5. Relationship between the infestation level and the fruit wall thickness (A) and single weight (B). Each data point represents the number of infested fruits per plant in each fruit wall thickness or weight (means ± SE). Line was fitted using linear regression analysis.
Fig. 1 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 1. (a) Adult pepper weevil feeding on the stalk of a pepper fruit; (b) young and full grown larvae inside a pepper fruit; (c) pupa inside the fruit; and (d) adult weevil exit holes in pepper fruits.
Galvanising the Open Access Community: A Study on the Impact of Plan S - Data and Code
<div> <div> <div> <div> <p>This repository contains the datasets and code underpinning <em>Chapter 3 "Counterfactual Impact Evaluation of Plan S"</em> of the report <em>"Galvanising the Open Access Community: A Study on the Impact of Plan S"</em> commissioned by the cOAlition S to scidecode science consulting.</p> <p>Two categories of files are part of this repository:</p> <p><strong>1. Datasets <br></strong></p> <p>The 21 CSV source files contain the subsets of publications funded by the funding agencies that are part of this study. These files have been provided by <em>OA.Works</em>, with whom scidecode has collaborated for the data collection process. Data sources and collection and processing workflows applied by <em>OA.Works</em> are described on their website and specifically at <a href="https://about.oa.report/docs/data">https://about.oa.report/docs/data</a>.</p> <p>The file "plan_s.dta" is the aggregated data file stored in the format ".dta", which can be accessed with <em>STATA </em>by default or with plenty of programming languages using the respective packages, e.g., <em>R</em> or <em>Python</em>. </p> <p><strong>2. Code files</strong></p> <p>The associated code files that have been used to process the data files are:</p> <pre> - data_prep_and_analysis_script.do<br> - coef_plots_script.R</pre> <p>The first file has been used to process the CSV data files above for data preparation and analysis purposes. Here, data aggregation and data preprocessing is executed. Furthermore, all statistical regressions for the ounterfactual impact evaluation are listed in this code file. The second code file "coef_plots_script.R" uses the computed results of the counterfactual impact evaluation to create the final graphic plots using the <em>ggplot2 </em>package.</p> <p>The first ".do" file has to be run in STATA, the second one (".R") requires the use of an integrated development environment for R. </p> Further Information are avilable in the final report and via the followng URLs:<br> <pre><a href="https://www.coalition-s.org/">https://www.coalition-s.org/</a> <a href="https://scidecode.com/">https://scidecode.com/</a> <a href="https://oa.works/">https://oa.works/</a> <a href="https://openalex.org/">https://openalex.org/</a><br><a href="https://sites.google.com/view/wbschmal">https://sites.google.com/view/wbschmal</a> </pre> </div> </div> </div> </div>
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </p>
Fig. 3 in Opening a can of lungworms: Molecular characterization of Dictyocaulus (Nematoda: Dictyocaulidae) infecting North American bison (Bison bison)
Fig. 3. Maximum likelihood analysis of cytochrome oxidase c subunit 1 (cox1) sequence data of Dictyocaulus spp. Analysis was run with TN93 + G as best nucleotide substitution model and 1,000 bootstraps. Angiostrongylus vasorum = outgroup.
Fig. 1 in Opening a can of lungworms: Molecular characterization of Dictyocaulus (Nematoda: Dictyocaulidae) infecting North American bison (Bison bison)
Fig. 1. Sites of bison fecal collections; WHH: White Horse Hill National Game Preserve; RMA: Rocky Mountain Arsenal National Wildlife Refuge; NBR: National Bison Range; NSM: Neal Smith National Wildlife Refuge; WMW: Wichita Mountains Wildlife Refuge.
Fig. 2 in Opening a can of lungworms: Molecular characterization of Dictyocaulus (Nematoda: Dictyocaulidae) infecting North American bison (Bison bison)
Fig. 2. Maximum likelihood analysis of internal transcribed spacer 2 (ITS2) sequence data of Dictyocaulus spp. Analysis was run with T92 as best nucleotide substitution model and 1,000 bootstraps. Angiostrongylus vasorum = outgroup.
FIGURE 1 in Paleontologia Electronica is still number one for new open access fossil species
FIGURE 1. Eekaulostomus cuevasae, an extinct armored trumpetfish, and the top open access fossil taxon of 2017 (Farke, 2017). Reproduced from Cantalice and Alvarado-Ortega (2016). 2. Illustration of trumpetfish from Brian Engh, http://dontmesswithdinosaurs.com.
Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches
<p>The primary goal of this dataset is to enhance the spatial and temporal distribution of emissions from civil aviation (NFR1.A.3.a), road transport (NFR1.A.3.b), railways (NFR1.A.3.c), and military aviation (NFR1.A.5), using Portugal as case study. For more information, please refer to the published article “Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches” (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.102097" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.102097</span></span></a>). This dataset contains the following folders and files:</p> <p><strong>1. Spatial_Location</strong></p> <p> 1.1. NFR1_A_3_a.gdb: Geodatabase containing the locations of Portuguese airports and aerodromes.</p> <p> 1.2. NFR1_A_3_b.gdb: Geodatabase containing the locations of Portuguese roads.</p> <p> 1.3 NFR1_A_3_c.gdb: Geodatabase containing non-electrified Portuguese railways and train station locations.</p> <p> 1.4 NFR1_A_5.gdb: Geodatabase containing the locations of Portuguese military airport facilities.</p> <p><strong>2. Temporal_Profiles</strong></p> <p><em> 2.1. Daily</em></p> <p> 2.1.1. Daily_NFR1_A_3_a.csv: This csv file contains the daily movements profiles of civil aviation sites in Portugal.</p> <p><em> 2.2. Hourly</em></p> <p> 2.2.1. Hourly_NFR1_A_3_b.txt: This txt file contains the hourly road traffic volume profiles for the road transport activities in Portugal at different locations (BigAir column).</p> <p> 2.2.2. Hourly_NFR1_A_3_c.txt: This text file contains the hourly railway profile in Portugal, categorized by line and train station.</p> <p><strong>3. Emission_Factors</strong></p> <p> 3.1. EF_NFR1_A_3_a.xlsx: This Excel file contains emission factors for civil aviation activities, categorized by technology, flight phase, fuel, and pollutant. Additionally, it includes information about engines and aircraft.</p> <p> 3.2. EF_NFR1_A_3_b.xlsx: This Excel file contains emission factors for road transport activities, categorized by vehicle type, technology, fuel, abatement, and pollutant. Emission factors for road resuspension are not provided because the papers using this dataset are still under review.</p> <p> 3.3. EF_NFR1_A_3_c.xlsx: This Excel file contains emission factors for railways activities, categorized by technology, fuel, and pollutant.</p> <p> 3.4. EF_NFR1_A_5.xlsx: This Excel file contains emission factors for military aviation activities, categorized by fuel, and pollutant.</p> <p><strong>4. Other_Info</strong></p> <p> 4.1 NFR1_A_3_b: This folder contains information organized by road segments, including fuel consumption (in the “FuelConsumption” folder), hourly meteorology (in the “Meteorology” folder), population data (in the “Population” folder), daily traffic volume (in the “TrafficVolume” folder), vehicle categories (in the “VehicleCategory” folder), and vehicle classes (in the “VehicleClasses” folder). Additionally, it includes the link between road traffic volume measurement points and the Portuguese road network (in the “sensorsVSroads” folder)</p>
Data curation materials in "Daily life in the Open Biologist's second job, as a Data Curator"
<p>This is the supplementary material accompanying the manuscript "Daily life in the Open Biologist’s second job, as a Data Curator", published in <a href="https://doi.org/10.12688/wellcomeopenres.22899.1">Wellcome Open Research</a>. </p> <p>It contains:</p> <p><strong>- Python_scripts.zip</strong>: Python scripts used for data cleaning and organization:</p> <p> -add_headers.py: adds specified headers automatically to a list of csv files, creating new output files containing a "_with_headers" suffix.</p> <p> -count_NaN_values.py: counts the total number of rows containing null values in a csv file and prints the location of null values in the (row, column) format.</p> <p> -remove_rowsNaN_file.py: removes rows containing null values in a single csv file and saves the modified file with a "_dropNaN" suffix.</p> <p> -remove_rowsNaN_list.py: removes rows containing null values in list of csv files and saves the modified files with a "_dropNaN" suffix.</p> <p><strong>- README_template.txt</strong>: a template for a README file to be used to describe and accompany a dataset. </p> <p><strong>- template_for_source_data_information.xlsx</strong>: a spreadsheet to help manuscript authors to keep track of data used for each figure (e.g., information about data location and links to dataset description).</p> <p><strong>- Supplementary_Figure_1.tif</strong>: Example of a dataset shared by us on Zenodo. The elements that make the dataset FAIR are indicated by the respective letters. Findability (F) is achieved by the dataset unique and persistent identifier (DOI), as well as by the related identifiers for the publication and dataset on GitHub. Additionally, the dataset is described with rich metadata, (e.g., keywords). Accessibility (A) is achieved by the ease of visualization and downloading using a standardised communications protocol (https). Also, the metadata are publicly accessible and licensed under the public domain. Interoperability (I) is achieved by the open formats used (CSV; R), and metadata are harvestable using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), a low-barrier mechanism for repository interoperability. Reusability (R) is achieved by the complete description of the data with metadata in README files and links to the related publication (which contains more detailed information, as well as links to protocols on protocols.io). The dataset has a clear and accessible data usage license (CC-BY 4.0).</p>
WaivOps RGTM-PNO: Open Audio Resources for Machine Learning in Music
<p><strong>RGTM-PNO Dataset</strong></p> <p>RGTM-PNO is an open audio dataset featuring a collection of vintage piano songs in the style of ragtime, a genre that flourished around the turn of the 20th century. The dataset contains 262 audio tracks recorded in uncompressed stereo WAV format, synthetically generated using a custom soundfont and MIDI files sourced from public resources online.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include audio classification, automatic music transcription (ADT), music information retrieval (MIR), melody analysis, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>262 piano songs (approximately 13.5 hours)</li> <li>16-bit WAV format</li> <li>Tempo: 120bpm (live performance in absolute time)</li> <li>Variational chorus detuning (vintage piano sound)</li> <li>Paired audio and MIDI data</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. The audio recordings were sonified from MIDI files containing historical musical compositions believed to be in the public domain and copyright free.</p> <p>The RGTM-PNO dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-TR8">GitHub repository</a>.</p>
Publications of professors and teachers of the Ukrainian Free University (1921 - 1925), placed in the Open Access
<p>The publications of scientists of the Ukrainian Free University for the period from 1921 to 1925 inclusive, placed in the Open Access with the indication of electronic libraries in which they were placed, are presented. </p>
Fig. 1 in Effects of irrigation method on pollination and pollinators (Hymenoptera: Apoidea) in an open-field tomato crop
Fig. 1. Fruit set (A) and weight of fruit (B) in relation to type of irrigation and type of pollination. OM = open + mechanical pollination.
A Test Dataset for Open Chrono-Morph Viewer (OCMV)
<p><span>Open Chrono-Morph Viewer (OCMV) is an open-source image visualization and animation platform handling volumetric time series, especially those containing unequally sampled volumes, such as those produced by 4D (3D+time) cardiac alignment. OCMV is available at <a href="https://github.com/ShangWangLab/OpenChronoMorphViewer"><span>https://github.com/ShangWangLab/OpenChronoMorphViewer</span></a>. This dataset is for demonstrating OCMV’s unique timeline features. It shows the dynamics of the embryonic mouse heart around embryonic day 8.5, consisting of 150 NRRD volumes spanning two timescales. The fast timescale represents the heartbeat phase over the complete cardiac cycle, while the slow timescale represents the development at three timepoints spanning two hours. Across the three developmental timepoints, the number of volumes sampling the heartbeat changes, as does the volume size (number of voxels). The data have two channels: the first channel represents the tissue structure, and the second channel represents the blood flow velocity component along the vertical axis spanning ±9.8 mm/s. The acquisition and processing of this dataset followed the methods described in <a href="https://doi.org/10.1364/BOE.475027">https://doi.org/10.1364/BOE.475027</a>. All animal manipulations were approved by the Institutional Animal Care and Use Committee at Baylor College of Medicine, and the experiments followed the approved procedures and guidelines.</span></p> <p><span>Acknowledgement: We thank Dr. Irina V. Larina (Baylor College of Medicine) for her support with the experiment.</span></p>
5G Campus Network QoS Dataset for Open-Source gNB Implementations
<p>This 5G campus network dataset contains 6 distinct CSV files.<br>Three for each of the testbeds located at either the Norwegian University of Science and Technology(NTNU) or the University of Wuerzburg(WUE).<br>all_Packets: contains all One-Way-Delay measurement packets. Columns: src, Timestamp, SourceIPOuter, DestinationIPOuter, SourceIPInner, DestinationIPInner, PacketSize, SeqNum, iat, trel, gnb, sdr, bw, slots, ratio, pdist, piat, psize, pnpak, rep, scenario, direction<br>Packets_with_IATs: contains all One-Way-Delay measurement packets, with the Interarrival-times already calculated and added as column. Columns: src, Timestamp, SourceIPOuter, DestinationIPOuter, SourceIPInner, DestinationIPInner, PacketSize, SeqNum, iat, trel, gnb, sdr, bw, slots, ratio, pdist, piat, psize, pnpak, rep, scenario, direction<br>all_Throughput: contains all output information from the throughput measurements conducted with iperf3. Columns: mbpsactual_uplink, mbpsactual_downlink, meanjitterms_uplink, meanjitterms_downlink, meanloss_uplink, meanloss_downlink, gnb, sdr, bw, slots, ratio, mbpsoffered_uplink, mbpsoffered_downlink, rep, scenario</p> <p>src: Source Device<br>Timestamp: Timestamp from wireshark<br>SourceIPOuter: Source IP of the outer GTP packet<br>DestinationIPOuter: Destination IP of the outer GTP packet<br>SourceIPInner: Source IP of the inner GTP packet<br>DestinationIPInner: Destination IP of the inner GTP packet<br>PacketSize: Size of the packet<br>SeqNum: Sequence number injected during traffic generation<br>iat: Interarrival-time of the packet<br>trel: relative timestamp<br>gnb: gNB implementation used<br>sdr: SDR used<br>bw: configured bandwidth<br>slots: configured DL slots<br>ratio: configured DL:UL ratio<br>pdist: chosen distribution of the packet generation (deterministic/negative-exponential time between transmission of the generated packets)<br>piat: chosen interarrival -time during packet generation<br>psize: chosen packet size mode<br>pnpak: total amount of packets generated for this measurement run<br>rep: repetition of measurement<br>scenario: measurement scenario<br>direction: direction of traffic</p> <p>mbpsactual_uplink: actual uplink throughput in MBps<br>mbpsactual_downlink: actual downlink throughput in MBps<br>meanjitterms_uplink: actual uplink jitter in ms<br>meanjitterms_downlink: actual downlink jitter in ms<br>meanloss_uplink: mean loss in uplink<br>meanloss_downlink: mean loss in downlink<br>mbpsoffered_uplink: configured uplink throughput in MBps<br>mbpsoffered_downlink: configured downlink throughput in MBps</p> <p>This dataset stems from the Paper "Parameterizing 5G New Radio: A Comparative Measurement Study on Throughput and Delay" </p>
Data from Open Data RDW: Gekentekende_voertuigen / Gekentekende_voertuigen_brandstof
<p>Data collected from the Open Data RDW repository. Data export is very slow.</p> <p> </p> <p>Web links for the two datasets:</p> <ul> <li><a href="https://opendata.rdw.nl/Voertuigen/Open-Data-RDW-Gekentekende_voertuigen/m9d7-ebf2/about_data">Open Data RDW: Gekentekende_voertuigen | Open Data | RDW</a></li> <li><a href="https://opendata.rdw.nl/Voertuigen/Open-Data-RDW-Gekentekende_voertuigen_brandstof/8ys7-d773/data_preview">Open Data RDW: Gekentekende_voertuigen_brandstof | Open Data | RDW</a></li> </ul>
Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"
<p>Contains research data underlying the following research paper:</p> <blockquote> <p>Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798</p> </blockquote> <p>The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</p>
Daten zum Open Access in der deutschsprachigen Rechtswissenschaft (Update 2024)
<p>Die vorliegende Version eines Datensatzes zum Open Access in der Rechtswissenschaft des deutschen Sprachraums (DE/CH/AU/LI) erweitert die <a href="https://doi.org/10.5281/zenodo.7796493">vorige Version</a> um fünf weitere Zeitschriften und berichtigt einzelne andere Einträge (bspw. media|lex). Damit liegt eine zum 1.10.2024 aktualisierte Liste der juristischen Internetzeitschriften im deutschen Sprachraum vor, auf die sich die zeitgleich aktualisierte Fassung des Überblicksbeitrags "<a href="https://open-access.network/informieren/open-access-in-fachdisziplinen/rechtswissenschaft">Open Access in der Rechts­wissen­schaft</a>" stützt.</p>
Open Sourced Database for CEO Dismissal 1992-2022
<h2><a href="https://forms.gle/DE4uDZFLFcWDCCZ58" target="_blank" rel="noopener">There is a newer version of this dataset available online. Click this link for the hosting site. </a></h2> <p>We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the documentation file.</p> <p>This version updates the set to the current turnovers as of May 1, 2023 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log. The file was shared and completed on November 9, 2023</p> <p>If you would like to get an email notification when we update the database, <a href="https://docs.google.com/forms/d/e/1FAIpQLSfiZZHwyeWYEZ5fOT1_RygH-ComG9ltad5IUUY60Fsw9z3hZg/viewform">sign-up here</a>. We're happy to let you know when it is updated. </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.