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1,131 results for “Berlin”
Data from Phenocam (PHE) measurements at Berlin-Technical University of Berlin (BETUCC) from 2023-06-01 to 2023-12-31 [RAW]
<p>Original phenocam images separated into near-infrared (NIR) and visible (VIS).</p>
Data from Phenocam (PHE) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2023-01-01 to 2023-12-31 [RAW]
<p>Original phenocam images separated into near-infrared (NIR) and visible (VIS).</p>
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
PM_152256_D_Berlin
<u>File Name</u>: PM_152256_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Maria in Verehrung/Maria in aanbidding, (fragment), Dieric Bouts, ca 1470 <br><u>Description</u>: Painting Dieric Bouts The virgin in Adoration Ca 1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152258_D_Berlin
<u>File Name</u>: PM_152258_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Christus in het huis van Simon de farizeër, Dierick Bouts, ca 1465-1470 <br><u>Description</u>: Painting Christ in the House of Simon the Pharisee Dierick Bouts Ca 1465-1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152259_D_Berlin
<u>File Name</u>: PM_152259_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Christus in het huis van Simon de farizeër, Dierick Bouts, ca 1465-1470 <br><u>Description</u>: Painting Christ in the House of Simon the Pharisee Dierick Bouts Ca 1465-1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152260_D_Berlin
<u>File Name</u>: PM_152260_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Christus in het huis van Simon de farizeër, Dierick Bouts, ca 1465-1470 <br><u>Description</u>: Painting Christ in the House of Simon the Pharisee Dierick Bouts Ca 1465-1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152261_D_Berlin
<u>File Name</u>: PM_152261_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Christus in het huis van Simon de farizeër, Dierick Bouts, ca 1465-1470 <br><u>Description</u>: Painting Christ in the House of Simon the Pharisee Dierick Bouts Ca 1465-1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152257_D_Berlin
<u>File Name</u>: PM_152257_D_Berlin.jpg <br><u>Sublocation</u>: Gemäldegalerie, Staatliche Museen zu Berlin <br><u>Location</u>: Berlin <br><u>Province</u>: Berlin, Berlin <br><u>Country</u>: Germany <br><u>Header</u>: Schilderij, Maria in Verehrung/Maria in aanbidding, (fragment), Dieric Bouts, ca 1470 <br><u>Description</u>: Painting Dieric Bouts The virgin in Adoration Ca 1470 <br><u>Keywords</u>: Berlin, Cultural heritage, Europe, Germany, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: Dieric Bouts (ca 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Berlin Sparql Benchmark (BSBM): Evolving Graph Simulation
<p>The Berlin SPARQL Benchmark (BSBM) is a suite of benchmarks built around an e-commerce use case [1]. We generated 21 versions of the dataset with different scale factors. The first dataset, with a scale factor of 100, contains about 7,000 vertices and 75,000 edges. We generated versions with scale factors between 2,000 and 40,000 in steps of 2,000. The largest dataset contains about 1.3 M vertices and 13 M edges. For our experiments in [2], we first use the different versions ordered from smallest to largest (version 0 to 20) to simulate a growing graph database. Subsequently, we reverse the order to emulate a shrinking graph database. Over all versions, the mean degree is 8.1 (+- 0.5), the mean in-degree is 4.6 (+- 0.3), and the mean out-degree is 9.8 (+- 0.2).</p> <p>1. <a href="https://dblp.uni-trier.de/pid/b/ChristianBizer.html">Christian Bizer</a>, <a href="https://dblp.uni-trier.de/pid/47/7466.html">Andreas Schultz</a>: The Berlin SPARQL Benchmark. <a href="https://dblp.uni-trier.de/db/journals/ijswis/ijswis5.html#BizerS09">Int. J. Semantic Web Inf. Syst. 5(2)</a>: 1-24 (2009)</p> <p>2. <a href="https://dblp.uni-trier.de/pid/222/6353.html">Till Blume</a>, <a href="https://dblp.uni-trier.de/pid/r/DavidRicherby.html">David Richerby</a>, <a href="https://dblp.uni-trier.de/pid/06/2380.html">Ansgar Scherp</a>: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. <a href="https://dblp.uni-trier.de/db/conf/cikm/cikm2020.html#BlumeRS20">CIKM 2020</a>: 75-84</p>
5GENESIS-BERLIN-2021-BITMOVIN-V1.0
<p>Exported data from Bitmovin Analytics: a comprehensive video analytics system facilitating QoE assessment.</p>
5GENESIS-BERLIN-2021-NWI-V1.0
<p>Information about clients´ network connection in terms of general connection type, such as Wi-Fi or cellular.</p>
5GENESIS-BERLIN-2021-POSITION-V1.0
<p>Per-client position trace of longitute, latitude, and altitude, using available geolocation techniques, primarily GPS.Per-client position trace of longitute, latitude, and altitude, using available geolocation techniques, primarily GPS.</p>
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Berlin State Library (2024). Metadata of the Digitized Collections of the Berlin State Library (SBB)
<p>The motivation for creating this dataset was to enable research on the basis of metadata which are available in a cultural heritage institution on a large scale. Libraries such as the Staatsbibliothek zu Berlin – Berlin State Library (SBB) typically provide three kinds of data: Images (scans of books, illustrations contained in the scanned material, or else), texts (OCR'd from digitized books or manuscripts), and metadata. However, metadata form an underresearched resource, which is lamentable: These metadata are of a high quality since they have been established by trained librarians, archivists, or other cultural heritage practitioners. The publication of a set of metadata of more than 200.000 works aims therefore at providing an underresearched high-quality type of data. The basic interest of the funder in this data publication is the stimulation of innovation.</p> <p>The dataset consists of a single table containing the metadata of all 219.419 works which were available in the Digitized Collections of the Berlin State Library (SBB) on July 29th, 2024. The size of the .parquet file is about 46 MB.</p>
Drawing of Karabel ("Sesostris") found in the Richard Lepsius estate at the Staatsbibliothek zu Berlin
<p>Possible copy of Texier’s original 1839 drawing housed in the Lepsius legacy in the Manuscript Department at the State Library in Berlin The folder containing the drawings is labelled ‘Dessins envoyés par M. H. Guys à M. Lajard’, but this probably refers to the drawings of the reliefs from Nahr el-Kalb, which are also kept in the Lepsius collection. There are no signatures on the document, but the Berlin drawing (an identical pen version also exists) could be a copy of Texier’s original 1839 drawing, as it closely resembles that published by Texier in 1849.</p>
Metadata of the "Alter Realkatalog" (ARK) of Berlin State Library (SBB) Version 2 - August 2025
<p>This dataset was created with the intent to provide a single larger set of metadata from Berlin State Library for research purposes and the development of AI applications.</p> <p>The dataset comprises descriptive metadata of 2.639.554 titles derived from the union catalogue K10plus, a database with about 200 million records from libraries across 11 German states. Selected are all records that include system entries ("Systemstellen") from the historical classification of the "Alter Realkatalog" (ARK), a subject catalogue of the Staatsbibliothek zu Berlin – Berlin State Library. They refer to publications from 1501 to 1955 and reproductions thereof. The title data contain subject headings and BK classmarks that have been transmitted onto them from the "<a href="https://ark.staatsbibliothek-berlin.de/">Historische Systematik</a>", the online representation of the ARK classification.</p>
Air quality trends for Berlin and Hamburg (2021-2022)
<p><strong>Air pollution constitutes the greatest environmental challenge in Europe (EEA, 2023).</strong> In the context of <strong>CALLISTO</strong>, an EU-funded project, and most specifically its pilot use case "Sensor Journalism", air pollution is studied from a journalistic point of view through an integrated solution comprised of multiple data visualisation tools. One of these tools is <strong>CALLISTO's Geospatial Business Intelligence (GeoBI) tool</strong>, which provides various visualisations of primarily air quality data and its purpose is to enable journalists identify air quality events and trends to build their stories.</p> <p>The GeoBI tool harnesses data from various sources <em>(e.g., official ground-based monitoring stations, low-cost sensing networks, satellites, social media)</em>, providing various visual elements <em>(e.g., figures, maps, pipe graphs, etc.) </em>on historical, near real-time and forecast air quality data, and has the ability to extract and interpret the data in some extent. For example, it takes into consideration the concentrations of the available air pollutants to display an Air Quality Index accompanied with a characterisation of the air quality in a specific location. Further data sources on socio-economic and environmental factors <em>(e.g., roadworks data) </em>are also explored.</p> <p>The <strong>files</strong> provided here include <strong>trends</strong> <strong>of concentrations of specific air pollutants for the areas of Berlin and Hamburg during 2021 and 2022 (graphs & csv data)</strong>. The data derive from the official air quality monitoring stations DEBE065 and DEHH008, in Berlin and Hamburg respectively, and are taken from <a href="https://openaq.org/">OpenAQ</a> (i.e., one of the data sources feeding the GeoBI tool). </p> <p><strong>Main information regarding the provided files:</strong></p> <p><strong>1) Berlin AQ trends 2021-2022</strong></p> <ul> <li>Station ID: DEBE065</li> <li>Location: 52.513379, 13.469294</li> <li>Year of measurements: 2021, 2022</li> <li>Pollutants measured: PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, CO, O<sub>3</sub></li> </ul> <p><strong>2) Hamburg AQ trends 2021-2022</strong></p> <ul> <li>Station ID: DEHH008</li> <li>Location: 53.564202, 9.967863</li> <li>Year of measurements: 2021, 2022</li> <li>Pollutants measured: PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, O<sub>3, </sub>SO<sub>2</sub></li> </ul> <p>As the CALLISTO project, and thus the GeoBI tool, progresses, additional graphs related to air quality trends may be generated, which will also be made available to everyone.</p> <p> </p> <p><em>The CALLISTO project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101004152.</em></p>
Seals from the Staatsbibliothek zu Berlin and their automated detection
<p>This repository is a first step towards the compilation of an Islamic seals database comprising the following components:</p> <p>- digital photos of pages with seals, retraceable to the original artifact.</p> <p>- cut-outs of seals.</p> <p>- scripts for aggregating these resources.</p> <p>- scripts for automatically identifying similar/same seals.</p> <p>This repository took the collection at the StaBi as a first case. As such, this repository can be used as a data set for training purposes.</p> <p><strong>Scripts for seal detection</strong></p> <p>This is very basic still, and the examples show how it can be build out in different directions. The seal was actually taken from an entirely different collection. It seems the seal itself is not in this collection (or not in this orientation?) but clearly it can already function somewhat as an archetype to catch any stamps. Note that it only highlights the most likely candidate on a page, hence its singling out of only one seal.</p> <p><strong>Images from the Staatsbibliothek zu Berlin</strong></p> <p>These images were extracted from the Staatsbibliothek zu Berlin digital collections website. They can be retraced to their origin as follows: the PPN number is an identification for the object. The following number identifies the page.</p> <p>For a direct verification of the image, use the IIIF server by reconstructing the URL with this formula: `https://content.staatsbibliothek-berlin.de/dms/` then the PPN number, then `/full/0/` then the page number ending with `.jpg`</p> <p>The associated catalog page and manuscript viewer can be found by reconstruction the URL with this formula: `https://digital.staatsbibliothek-berlin.de/werkansicht?PPN=` followed by the PPN number.</p> <p>These images were assumed to be published by the StaBi and/or Stiftung Preußischer Kulturbesitz in the public domain per https://digital.staatsbibliothek-berlin.de/nutzungsbedingungen They have been brought together here strictly for research purposes with no further rights claimed.</p> <p><strong>Scripts for the StaBi</strong></p> <p>I used two scripts to automatically get 570 pages that supposedly contain Islamic seals. I also did some additional things to get everything working, for getting the right URLs and massaging the URLs into usable shapes.</p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></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.