Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,245
datasets available to search
ShareScore release 0.7.1
Dataset results
1,245 results for “dating”
Towards new demography proxies and regional chronologies: Radiocarbon dates from archaeological contexts located in the Czech Republic covering the period between 10,000 BC and AD 1250 (dataset)
<p>The dataset was created within the project “<em>Land use, social transformations and woodland in Central European Prehistory. Modelling approaches to human-environment interactions</em>” funded by the Czech Science Foundation (19-20970Y). This dataset represents the largest and the most comprehensive collection of archaeological radiocarbon dates from the Czech Republic to date. The dataset offers 1579 samples from 347 archaeological sites dating from Early Mesolithic (10 000 BC) to Medieval Period (AD 1250). Published in a simple spreadsheet format, the database offers researchers a quick tool for further analyses. It is important to highlight that dates we collected originated only from archaeological contexts, which means that we have excluded some radiocarbon dates produced through palaeoecological research without a direct relationship to past human activities, such as pollen records or samples from fossilized trees in river beds. The dataset is intended to be used for demographic modelling of population numbers during periods without written records, i.e. prehistory.</p>
Annotation of the the assembled genome of Fusarium oxysporum f. sp. albedinis strain 133, the causal agent of date palm dieback.
<p>Annotation of the the assembled genome of <em>Fusarium oxysporum f. sp. albedinis</em> strain 133 (Khayi et al., 2020). Gene prediction and annotation were carried out using funnotate pipeline v1.8.1 (Stajich, 2020), which includes masking, ab initio gene-prediction training, using Augustus and Genmark, with the EST dataset reported to the Ganoderma mycocosm repository, gene prediction, and the assignment of functional annotation to protein-coding gene models.</p>
Dataset: Human-vegetation dynamics in Holocene South-Eastern Norway based on radiocarbon-dated charcoal from archaeological excavations
<p>The repository contains radiocarbon data used in the paper <em>Human-vegetation dynamics in Holocene South-Eastern Norway based on radiocarbon-dated charcoal from archaeological excavations</em>, written by By Axel Mjærum, Kjetil Loftsgarden, and Steinar Solheim (University of Oslo).</p> <p>The paper is accepted for publication in The Holocene.</p> <p>ABSTRACT: Charcoal from archaeological contexts differs from off-site pollen samples as it is mainly a product of intentional human action. As such, analysis of charcoal from excavations is a valuable addition to studies of past vegetation and the interaction between humans and the environment. In this paper, we use a dataset consisting of 6,186 dated tree species samples from 1,239 archaeological sites as a proxy to explore parts of the Holocene forest development and human-vegetation dynamics in South-Eastern Norway.</p> <p>From the middle of the Late Neolithic (from <em>c</em>. 2000 BC) throughout the Early Iron Age (to <em>c</em>. AD 550) the region’s agriculture is characterized by fields, pastures, and fallow. Based on our data, we argue that these practices, combined with forest management, clearly altered the natural distribution of trees, and favoured some species of broadleaved trees. The past distribution of hazel (<em>Corylus avellana</em>) is an example of human impact on the vegetation. Today, hazel is not even among the 15 most common tree species, while it is one of the most prevalent species in the archaeological record before AD 550. The data indicate that this species was favoured already by the region’s Mesolithic hunter-fisher-gatherers, and that it was among the species that thrived extremely well in the early farming landscape. Secondly, our analysis also indicates that spruce (<em>Picea abies</em>) first formed large stands in the south-eastern parts of Norway <em>c</em>. 500 BC, centuries earlier than previously assumed. It is argued that this event, and a further westward expansion of spruce, was partly a consequence of a specific historical event – the first millennium BC farming expansion.</p>
AIDA (Archive of Italian radiocarbon DAtes)
<p>The archive <strong>AIDA</strong> provides a collation of <strong>4,629</strong> radiocarbon dates from <strong>1,050</strong> archaeological sites in Italy from the Late Mesolithic until Late Antiquity (11 - 1.5 kya BP). These dates have been collected from existing online digital archives, and electronic and print original publications. </p> <p>List of versions:</p> <ul> <li><strong>5.0</strong> 9 April 2022 — 589 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>4.0</strong> 3 March 2022 — 35 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>3.0</strong> 13 January 2022 — Removal of some duplicates and 4 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>2.0</strong> 13 January 2022 — Removal of some duplicates and 4 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>1.0</strong> 3 August 2021 — First public release of the dataset on Zenodo</li> </ul>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
40Ar/39Ar dating of the Barmur Group (Tjörnes beds), northern Iceland.
<p><sup>40</sup>Ar/<sup>39</sup>Ar radiometric ages and whole-rock major element data from four basaltic lavas that underlie, are intercalated with, and overlie the Barmur Group (Tjörnes beds), northern Iceland.</p>
Maṇibhadra inscription dated 100 10 2
<p>Spurious inscription on the pedestal of a <a href="https://doi.org/10.5281/zenodo.3555045">Maṇibhadra</a> image, allegedly dated saṃ 100 10 2 in the time of <a href="https://www.wikidata.org/wiki/Q470715">Kumāragupta</a>.</p> <p>Selected bibliography:</p> <ul> <li>D. M. Stadtner (2002) <em>Orientations</em> 33, 26-30</li> <li>Claudine Bautze-Pircon (2002) "The Rewriting of Indian Art History," <em>Orientations</em> 33, 69-70 noted as a forgery drawing on a variety of motifs and forms in known sculptures of the period, wherein Gouriswar Bhattacharya's assessment of the inscription as modern is also cited.</li> <li><a href="https://doi.org/10.5281/zenodo.3568108">R. Salomon (2003)</a> Report on Maṇibhadra inscription.</li> <li><a href="https://doi.org/10.5281/zenodo.8177147">Falk (2004) 167-176</a> wherein the inscription is accepted.</li> </ul>
Supplementary Date for a "Novel production of macrocapsules for self-sealing mortar specimens using stereolithographic 3D printers"
<p>This dataset was used for the publication of "<span>Novel production of macrocapsules for self-sealing mortar specimens using stereolithographic 3D printers". </span></p>
Release dates for role playing computer games (RPGs)
<p>Release date information from Steam using web data scraping through Python coding. <br>Code can be located on GitHub: <a href="https://github.com/kyoraven/genderGames">kyoraven/genderGames (github.com)</a></p>
Exploiting the Greenland volcanic ash repository to date caldera-forming eruptions and widespread isochrons during the Holocene
<p>Polar ice-cores have long been recognised as unrivalled repositories of past volcanic events. Although tephra products from local eruptions tend to dominate these records, improvements in micro-sampling and analytical techniques are uncovering a growing number of cryptotephras erupted from exceptionally distant volcanoes. We present a series of nine Middle Holocene cryptotephra deposits detected within the NGRIP ice-core that originate from five different volcanic regions across the Northern Hemisphere (Alaska, Cascades, Iceland, Japan, Kamchatka). Unique compositional signatures are employed to identify ash from three large caldera-forming events in Kamchatka (KS<sub>2 </sub>from Ksudach), the Cascades (Mazama) and North East Japan (Mashu), along with ash from the Hekla 4 eruption in Iceland. High-precision ice-core ages (adopting a 1950 datum for the GICC05 timescale assigned to the Greenland ice cores) are derived for each eruption: Hekla 4 (4325 ± 8 a b1.95k), KS<sub>2</sub> (7089 ± 26 a b1.95k), Mashu (i-f) (7473 ± 33 a b1.95k) and Mazama (7562 ± 35 a b1.95k), all of which can be employed as chronological fix-points in other proxy records where these deposits are also preserved. Four further cryptotephra deposits and one macro-deposit (in the GRIP ice core) are also identified and traced to sources in Iceland and Alaska. The cryptotephra originating from Alaska is correlated to a deposit identified in lake records from the Kenai Peninsula, thought to originate from Redoubt Volcano. The remaining four deposits are typical of the products of Katla, Grímsvötn and Veiðivötn in Iceland. This ensemble of mid-Holocene tephra deposits highlights the pivotal position of the Greenland ice-sheet and its ice-cores to capture deposition from the convergence of several far-travelled ash clouds. Precise age estimates derived from the annually resolved ice-core record greatly enhances the value of these tephra isochrons.</p> <p> </p>
Publication dates for ArXiv publication versions
<p>Lookup tables in plain JSON, mapping ArXiv publication version identifiers to their respective publications dates.</p> <p>The JSON files are archived in <em>arxiv-publication-dates-by-identifier-prefix.tar.gz</em>.<br>The archive contains files named after the date prefix of the ArXiv publication version identifiers they contain.<br>E.g., the file <em>1908.json</em> will contain the data for identifiers <em>1908.12345v1</em>, <em>1908.12345v2</em>, <em>1908.23456v1</em>, etc.<br>Publication dates are given in the format <em>YYYY-MM-DD</em>.</p> <h2>Reproducibility</h2> <p>The <a href="https://snakemake.readthedocs.io/">Snakemake</a> workflow that has produced this dataset has been archived and is available in <em>arxiv-publication-dates-workflow.tar.gz</em>.</p> <h3>Changes in version 1.2</h3> <p>Version 1.2 includes a JSON file that contains a JSON array with all file names included in the dataset: <em>file_names.json</em>.</p> <h3>Changes in version 1.1</h3> <p>For version 1.1, the dataset was extended manually to include a single missing date for <a href="https://arxiv.org/abs/0906.3421v3">arXiv:0906.3421v3</a>: <em>2010-02-02</em>. As of 2024-05-13, the date for the respective version had not been provided in the <em>arXivRaw</em> OAI-PMH data (<a href="http://export.arxiv.org/oai2?verb=GetRecord&identifier=oai:arXiv.org:0906.3421&metadataPrefix=arXivRaw">http://export.arxiv.org/oai2?verb=GetRecord&identifier=oai:arXiv.org:0906.3421&metadataPrefix=arXivRaw</a>).</p> <h3>Running the workflow</h3> <p>To reproduce the dataset on a Linux machine, you need a version of the <a href="https://conda-forge.org/"><em>conda</em></a> package manager installed on your system.</p> <p>Run the following:<br><br></p> <pre><code># Extract the archived workflow tar -xf my-workflow.tar.gz # Create conda environment from lock file conda env create -n arxiv-metadata --file conda-environment.lock.yaml # Activate the environment conda activate arxiv-metadata # Optionally, dry-run the workflow snakemake -n # Produce the output files snakemake --keep-storage-local-copies --software-deployment-method conda -c <NUMBER OF CORES TO USE></code><br><br>Then, append the file <em>0906.json</em> (included in the <em>tar.gz</em> output) with value <em>2010-02-02</em> for a new key <em>0906.3421v3</em>.</pre> <h2>Workflow</h2> <p>To adapt/change the workflow, clone it from <a href="https://github.com/sdruskat/arxiv-publication-metadata">https://github.com/sdruskat/arxiv-publication-metadata</a>.<br>The workflow version used to produce this dataset is available at <a href="https://doi.org/10.5281/zenodo.11507183">https://doi.org/10.5281/zenodo.11507183</a>. </p>
Radiocarbon Dates Iron Production Middle-Northern Sweden
<p>Radiocarbon dates analysed in the MA-thesis of Jonatan Rigvald, Uppsala university. This only represents a selection of the full database of radiocarbon dated iron production sites, to be publised (Hennius et al. in press).</p>
Collated set of all gravitational wave observations (to current date) from Wikipedia
<p>This is a dataset I needed but couldn't find. So, with the help of Chat GPT (and several hours ot time), I compiled this easy to access html file.</p> <p>It's not perfect but here it is for all of you to enjoy. </p>
MPS Data set with images of medieval charters for handwriting-style based dating of manuscripts
<pre>The MPS benchmark data set for handwritten manuscript dating ____________________________________________________________ This data set is collected for the Dutch NWO project: Medieval Paleographical Scale (MPS) by Petros Samara Project website: http://application02.target.rug.nl/monk/Projects/MPS/ Copyright (c) Huygensinstituut, Den Haag, 2016 University of Groningen, 2016. All rights reserved. Organisation of the data: Each .tar.gz file contains a number of NetPBM images. The format is chosen because of its simplicity. Also, there is no doubt about lossy compression in the processing chain. The file names are of the format 'MPS<year>_<seqnr>.ppm', for example, 'MPS1300_0056.ppm'. Note: the files are not in a separate directory, they will be extracted in place. However, due to the unique naming, there is no problem extracting them in one single current (destination) directory. The actual type of the image can be gray scale (.pgm) or color (.ppm), in '8-bit DirectClass' according to ImageMagick's 'identify' tool. The images were cropped out of larger photographs because of irrelevant elements such as a Kodak color calibrator and non-text content such as supporting surface (table) backgrounds, seals (emblems), ribbons, etc. No effort has been made to obtain a balanced set of samples over years: the given frequencies of occurrence in archives are used. There is evidently less data in years before 1375 A.D. while some periods provides us with ample data for historical reasons (e.g, 1450 A.D.). It would have been a pity if the scarce years had determined and limited the size of this data set. Selection criteria for data reduction, whether random or systematic, would have been arbitrary. In any case, these images were used in our publications, such that the performance results of future attempts on manuscript dating can be compared with earlier results. The performances that have been reached using our algorithms are in the order of an MAE (mean average error) of 10 years. If you have any questions, please contact us: Sheng He (heshengxgd@gmail.com) Petros Samara (petros.samara@huygens.knaw.nl) Jan Burgers (jan.burgers@huygens.knaw.nl) Lambert Schomaker (L.Schomaker@ai.rug.nl) Please cite our papers if you use this data set: [1] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Image-based historical manuscript dating using contour and stroke fragments. Pattern Recognition(PR), Vol. 59, pp. 159-171, 2016 [2] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Towards style-based dating of historical documents. International Conference on Frontiers in Handwriting Recognition(ICFHR), Crete, Greece, 2014 [3] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization IEEE Trans. on Image Processing, Vol. 25(11), Nov. 2016. http://ieeexplore.ieee.org/document/7551181/</pre> <p>Data are collected thanks to Dutch NWO grant project 380-50-006</p>
Radiocarbon dates for the Mesolithic-Neolithic transition in Iberia
<p>Radiocarbon dates to explore the chronology absolute of the Neolithic transition in the Iberian peninsula. A shapefile (GIS format has been upload too).</p>
Inundation maps of Donana for 23 dates within the period 2015/12/19 to 2017/08/20 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2015/12/19 to 2017/08/20 were generated for Donana based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Donana_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Danube Delta for 10 dates within the period 2016/10/05 to 2017/08/01 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/10/05 to 2017/08/01 were generated for Danube Delta based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Danube_Delta_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. The regions, which are manually denoted as affected by clouds, are denoted with 2. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Camargue for 47 dates within the period 2016/02/09 to 2018/06/19 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/02/09 to 2018/06/19 were generated for Camargue based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Camargue_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Detail of the Gangdhar inscription of Mayūrākṣaka with the date
<p>Figure 15 in</p> <p><em>To engrave his virtues on the disc of the moon… Inscriptions of the Aulikaras and Their Associates</em></p> <p>Dániel Balogh, 2019</p> <p>Detail of the Gangdhar inscription of Mayūrākṣaka with the date</p> <p>Siddham OB00069</p> <p>Siddham IN00076</p> <p>Photograph by the author, 2017. Courtesy of Government Museum, Jhalawar</p> <p>Above: composite of multiple closeup photos with grazing light. Below: eye tracing; clear lines shown in green; less distinct strokes in blue; restoration in red.</p>
Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 1 dated 2022-09-19]
<p>The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2022-09-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.</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.