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257 results for “data package”
Data package for modeling the journey of Colonel William Leake in the southern Mani Peninsula, Greece, using least-cost analysis
<p>Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, <em>Travels in the Morea: Volume I </em>(Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, 'Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,' is published in <em>Journal of Archaeological Science: Reports</em> and can be accessed here: <a href="http://doi.org/10.1016/j.jasrep.2019.01.014">https://doi.org/10.1016/j.jasrep.2019.01.014</a>. The article pre-print can be accessed here: <a href="https://works.bepress.com/rebecca-seifried/11/">https://works.bepress.com/rebecca-seifried/11/</a>.</p> <p>Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled 'Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)' (<a href="http://hdl.handle.net/10027/21274">https://hdl.handle.net/10027/21274</a>). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the 'CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,' whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula.</p> <p>This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
RECOMS data - Packages 1-3
<p>Data package 1: Gent case study</p> <p>Data package 2: Woluwe case study </p> <p>Data package 3 : Practitioners workshops</p>
lotus package data
<p>ewdiff_v0.tar.gz :the the difference of interpolated ew and theoretic ew at each grid point for every lines</p> <p>EWLIB_largergrid2_v0.h5: calculated EWs from MULTI</p> <p>lte_v0.tar.gz: interpolated models for lte</p> <p>nlte_v0.tar.gz : interpolated models for nlte</p> <p> </p>
Demonstration data for the python package applefy.
<p>Demonstration data for the python package applefy. </p> <p>30_data: Contains the NACO L' dataset of Beta Pic (planet removed) as used in the user documentation</p> <p>70_results: Contains the results of the user documentation tutorials</p> <p>laplace_lookup_tables.csv: Contains the lookup table for the LaplaceBootstrapTest</p>
CYJAX: A package for Calabi-Yau metrics with JAX [data & figures]
<p>Data of achieved accuracies of numerically approximated Calabi-Yau metrics as presented in the associated paper "CYJAX: A package for Calabi-Yau metrics with JAX" [<a href="https://arxiv.org/abs/2211.12520">2211.12520</a>].</p>
Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"
<p>Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".</p>
Simulated data for particle tracking and use in TrackMateR package
<p>The purpose of this dataset is to provide some example data for users of TrackMateR to use to become familiar with the package.</p> <p>The dataset contains three elements:</p> <ol> <li>Code to simulate some images (movies) of particle motions in 2D in Fiji. `particleSimulator.ijm` will generate images and ground truth positions of particles moving in six different modes (see below). An example output is given in `particleSimulatorOutput/`</li> <li>Code to automate the tracking of these images using TrackMate in Fiji. An example output is give in `TrackMateOutput/` These XML files can be used as the input in TrackMateR package.</li> <li> Outputs from TrackMate v 0.3.5 in `TrackMateROutput/`</li> </ol> <p>The simulated data is:</p> <ul> <li>Simulation A - particles moving in linear direction, variable but constant direction, high speed</li> <li>Simulation B - particles moving in linear direction, variable but constant direction, slow speed</li> <li>Simulation C - random motion high D (diffusion coefficient)</li> <li>Simulation D - random motion low D</li> <li>Simulation E - random motion, 50:50 mix of high and low D particles</li> <li>Simulation F - random motion, subdiffusive</li> </ul> <p>These TrackMate XML files can be processed using <a href="https://github.com/quantixed/TrackMateR">TrackMateR</a> as described <a href="https://quantixed.github.io/TrackMateR/">here</a>.</p>
SKYSPECTRA: an opensource data package for worldwide spectral daylight
<p>SKYSPECTRA is an open-source data package comprising spectral daylight measurements collected from various sources worldwide. The dataset encompasses measurements from both long-term measurement sites and specific periods or experiments. </p> <h2>Terms of use</h2> <p>For use of the dataset in research cite the paper titled <strong>skyspectra: an opensource data package of worldwide spectral daylight, </strong>presented at the CIE conference 2023, Slovenia, September 18-20 2023. The paper details the schema of the data package and will be publicly available soon.</p> <blockquote> <p><strong>Balakrishnan, P.</strong>, Diakite-Kortlever, A., Dumortier, D., Hernández-Andrés, J., Kenny, P., Maskarenj, M., Pierson, C., Thorseth, A., Xue, P., & Knoop, M. (2023). <em>SKYSPECTRA: An Opensource Data Package of Worldwide Spectral Daylight</em>. In <em>Proceedings of 30th session of CIE Conference, </em>15–23 September 2023. Ljubljana, Slovenia. DOI:10.25039/x50.2023.OP026</p> </blockquote> <div> <div> <div> <h2>Funding </h2> <div> <div> <div> <p>This project is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Individual Fellowship (grant agreement No. 101032279)</p> <h2>Data availability </h2> <ul> <li>Measurement data is between <code>360 nm to 800 nm</code> wavelength</li> <li>Timestamps with solar elevation angle <code>equal to or above 15 degrees</code></li> <li>Timestamps for two dates in the months of <code>March, June, September, and December</code> (when available)</li> <li>Timestamps with <code>clear and overcast sky conditions</code> (when available)</li> <li>Locations-<code>Berlin, Beijing, Granada, Singapore, Vaulx en Velin</code> (updates of other locations are ongoing) </li> </ul> <h2>Data structure </h2> <p>Measurement datasets include four data frames or tables in .csv format, each representing a distinct type of spectral daylight measurements.</p> <ul> <li><code><strong>spectral_horizontal_irradiance.csv:</strong></code> spectral irradiance measurements of global and diffuse visible radiation recorded on a horizontal plane</li> <li><code><strong>spectral_tilt_irradiance.csv:</strong> </code>spectral irradiance measurements of global and diffuse visible radiation recorded on a tilted plane (not available currently)</li> <li><code><strong>spectral_patch_radiance.csv:</strong></code> spectral radiance measurements of visible radiation from patches of sky</li> <li><strong><code>spectral_direct_irradiance.csv:</code></strong> spectral irradiance measurements of visible radiation directly from the sun</li> </ul> <p>Supplementary datasets include four data frames or tables in .csv format, each representing additional information for each measurement. </p> <ul> <li><code><strong>meta_location.csv:</strong></code> information about the geographical location and immediate environment of where the measurements were taken</li> <li><code><strong>meta_weather.csv:</strong></code> information on sky conditions when the measurements were taken.</li> <li><code><strong>meta_sun_positions.csv:</strong></code> information about sun angles and true solar time when the measurements were taken.</li> <li><strong><code>meta_measurement_parameters.csv:</code></strong> information about specific measurement parameters used for each measurement.</li> </ul> <h2>Data collection</h2> <p>This data package contains a subset of the worldwide spectral daylight measurements collected to explore geographic variations in daylight spectral characteristics, an ongoing effort led by the CIE Technical Committee (TC 3-60). For more information on the data collection process and the templates used refer to the paper <em>skyspectra: an opensource data package of worldwide spectral daylight </em>mentioned above. </p> </div> </div> </div> </div> </div> </div>
The US LTER Thesaurus: Contents and Keyword Use Statistics in LTER Data Packages in 2006 and 2018
This dataset contains raw data and statistical summaries that reflect use of keywords in LTER Datasets in May 2018 and 2006. Specific summaries include: Number of uses and number sites by keyword (LTERVocabKeywordSummary.csv), Summary of keyword use by data package (LTERVocabDataPackageSummary.csv), Summary of Keyword Use by LTER Site in 2018(LTERVocabSiteSummary.csv), Summary of Keyword Use by LTER Site in 2006(KeyStats2006.csv). Raw data includes XML files containing the US LTER Thesaurus in Moodle format and the ResultSet containing the information for each dataset from the Environmental Data Initiative PASTA repository.
A set of generated Instagram Data Download Packages (DDPs) to investigate their structure and content
<p><strong>Instagram data-download example dataset</strong></p> <p>In this repository you can find a data-set consisting of 11 personal Instagram archives, or Data-Download Packages (DDPs).</p> <p> </p> <p><strong>How the data was generated</strong></p> <p>These Instagram accounts were all new and generated by a group of researchers who were interested to figure out in detail<br> the structure and variety in structure of these Instagram DDPs. The participants user the Instagram account extensively for approximately a week. The participants also intensively communicated with each other so that the data can be used as an example of a network. </p> <p>The data was primarily generated to evaluate the performance of de-identification software. Therefore, the text in the DDPs particularly contain many randomly chosen (Dutch) first names, phone numbers, e-mail addresses and URLS. In addition, the images in the DDPs contain many faces and text as well. The DDPs contain faces and text (usernames) of third parties. However, only content of so-called `professional accounts' are shared, such as accounts of famous individuals or institutions who self-consciously and actively seek publicity, and these sources are easily publicly available. Furthermore, the DDPs do not contain sensitive personal data of these individuals. </p> <p><br> <strong>Obtaining your Instagram DDP</strong></p> <p>After using the Instagram accounts intensively for approximately a week, the participants requested their personal Instagram DDPs by using the following steps. You can follow these steps yourself if you are interested in your personal Instagram DDP. </p> <p>1. Go to www.instagram.com and log in<br> 2. Click on your profile picture, go to *Settings* and *Privacy and Security*<br> 3. Scroll to *Data download* and click *Request download*<br> 4. Enter your email adress and click *Next*<br> 5. Enter your password and click *Request download*</p> <p>Instagram then delivered the data in a compressed zip folder with the format **username_YYYYMMDD.zip** (i.e., Instagram handle and date of download) to the participant, and the participants shared these DDPs with us.</p> <p> </p> <p><strong>Data cleaning</strong></p> <p>To comply with the Instagram user agreement, participants shared their full name, phone number and e-mail address. In addition, Instagram logged the i.p. addresses the participant used during their active period on Instagram. After colleting the DDPs, we manually replaced such information with random replacements such that the DDps shared here do not contain any personal data of the participants.</p> <p> </p> <p><strong>How this data-set can be used</strong></p> <p>This data-set was generated with the intention to evaluate the performance of the de-identification software. We invite other researchers to use this data-set for example to investigate what type of data can be found in Instagram DDPs or to investigate the structure of Instagram DDPs. The packages can also be used for example data-analyses, although no substantive research questions can be answered using this data as the data does not reflect how research subjects behave `in the wild'. </p> <p><br> <strong>Authors</strong></p> <p>The data collection is executed by Laura Boeschoten, Ruben van den Goorbergh and Daniel Oberski of Utrecht University. For questions, please contact l.boeschoten@uu.nl. </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The researchers would like to thank everyone who participated in this data-generation project.</p>
SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data
<p>This dataset includes three high-density sleep EEG recordings of healthy participants, downsampled to 250 Hz and stored in FIF format:</p> <ol> <li>Nap recording of a young adult participant</li> <li>Overnight recording of a young adult participant</li> <li>Overnight recording of an older adult participant</li> </ol> <p>Additionally, the dataset includes three text files for each recording:</p> <ul> <li>bad_channels.txt: Indexes of noisy channels</li> <li>annotations.txt: Onset and duration of noisy temporal intervals</li> <li>staging.txt: Sleep staging vector</li> </ul> <p>The corresponding package can be found on <a href="https://github.com/NirLab-TAU/sleepeegpy">GitHub.</a></p> <p>For citation, please use:<br>Falach, R., G. Belonosov, J. F. Schmidig, M. Aderka, V. Zhelezniakov, R. Shani-Hershkovich, E. Bar, and Y. Nir. "SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data." Computers in Biology and Medicine 192 (2025): 110232.<br><a href="https://doi.org/10.1016/j.compbiomed.2025.110232" rel="nofollow">https://doi.org/10.1016/j.compbiomed.2025.110232</a></p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</p>
The Global ETOPO1_15second.nc data for CEGrplot package
<p>The Global ETOPO1_15second.nc data included in the package is sourced from NOAA National Centers for Environmental Information. <DOI: 10.25921/fd45-gt74></p>
HenDi-Data (data package)
<p>This is the data set of the project <a href="https://henze-digital.zenmem.de">Henze-Digital</a>. It contains project specific authority files (e.g., persons, organizations, places) and editions (e.g., letters, documents).</p>
The IBIS Challenge 2024 Complete Data Package
<p><strong>The IBIS Challenge 2024</strong></p> <p><em><strong>Complete Data Package, 17 Nov 2024</strong></em></p> <p><a href="https://ibis.autosome.org">https://ibis.autosome.org</a></p> <p>This repository provides the complete data of the Codebook/GRECO-BIT open challenge in Inferring Binding Specificities of human transcription factors from multiple experimental data types. Both the Leaderboard stage and the Final stage data are included. This repository is accompanied by the IBIS Benchmarking repo (doi:10.5281/zenodo.14176443), which includes the benchmarking suite and the challenge 'answers', i.e. the labeled test data.</p> <p><em>* Please refer to the supplied README for more details.</em></p> <p><em>** Revision 2: fixed the final/leaderboard attribution errors in the list of Codebook datasets used in IBIS.</em></p>
WeGA data package
<p>The WeGA data package is a collection of <a href="https://tei-c.org">TEI</a> and <a href="https://music-encoding.org">MEI</a> documents, constituting the digital edition of the <a href="https://weber-gesamtausgabe.de/">Carl-Maria-von-Weber-Gesamtausgabe</a>. The files are derived from our internal WeGA TEI format and are valid against TEI_all and mei_all respectively. WeGA element additions such as <code>tei:workName</code> are mapped to the more generic <code>tei:name</code> element, but no information was dropped. Internal references are provided via <code>@ref</code> attributes and full URIs, rather than magic <code>@key</code> attributes.</p> <p> </p> <p>1. Contents</p> <p>Every folder at the root level of the archive holds a distinctive WeGA document type</p> <ul> <li><code>addenda</code>: Addenda and Corrigenda concerning the printed volumes of the WeGA</li> <li><code>biblio</code>: bibliographic objects, forming our bibliography</li> <li><code>diaries</code>: Carl Maria von Weber's diaries</li> <li><code>documents</code>: various documents, not fitting any other category</li> <li><code>letters</code>: correspondence materials</li> <li><code>news</code>: website news, forming our "news" section on the website</li> <li><code>orgs</code>: organizations and institutions</li> <li><code>persons</code>: personographies</li> <li><code>places</code>: place descriptions</li> <li><code>thematicCommentaries</code>: small essays that comment on a collection of e.g. letters or diary entries</li> <li><code>var</code>: various website pages, e.g. API documentation</li> <li><code>works</code>: work descriptions</li> <li><code>writings</code>: writings, mostly reviews and historic news</li> </ul> <p> </p> <p>2. License</p> <p>The WeGA data package is released under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Enjoy!</p> <p> </p> <p>3. Contact</p> <p>This dataset is provided by the <a href="https://weber-gesamtausgabe.de/">Carl-Maria-von-Weber-Gesamtausgabe</a></p> <p>c/o Musikwissenschaftliches Seminar Detmold/Paderborn</p> <p>Hornsche Str. 39</p> <p>D–32756 Detmold</p> <p>info at weber-gesamtausgabe.de</p> <p> </p> <p>4. DOI</p> <p>10.5281/zenodo.3520701</p>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</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.