Find research datasets worth reusing

openother-openMay 2018View details →

Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data

This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data. ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.  Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)

opencc-by-4.0Apr 2019View details →

Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"

This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.

opencc-zeroSep 2019View details →

Crossref metadata of COCI bibliographic resources, as of November 2018 and LCC categories of the ISBN entities in the dataset

The all.zip CSV file (zipped) contains citation counts obtained from the November 2018 dump of COCI (https://doi.org/10.6084/m9.figshare.6741422.v3) and some metadata (title, DOI, number of authors, ISBN, ISBN of the container, type of the bibliographic resource) of the related citing and cited entities obtained by using the Crossref dump downloaded in October 2018 – which is the same dump used to create the COCI data. In addition, it contains all the Library of Congress Classification (LCC) categories associated with each ISBN in the previous dataset (file isbn_cat_lcc.csv), according to the data retrieved using the services at <a href="http://classify.oclc.org/classify2/api_docs/index.html">http://classify.oclc.org/classify2/api_docs/index.html</a>. Two ancillary mapping files have been also added: one (ddc_to_lcc_mapping.csv) for converting a Dewey Decimal Classification (DDC) categories into LCC categories, in the case the service mentioned above returned only DDC categories for some ISBN; the other (lcc_to_wos_mapping.csv) to map each LCC category into the related <a href="https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html">Web of Science research area</a>.

Test results for Competition for CPU resources experiments

Open the record for dataset details and reuse information.

opencc-by-nc-sa-4.0Aug 2024View details →

BacSPaD: A robust bacterial strains' pathogenicity resource based on integrated and curated genomic metadata

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at<a href="https://bacspad.altrabio.com/"> </a><a href="https://bacspad.altrabio.com/">https://bacspad.altrabio.com</a>.

answered questionnaire to Bachelor Thesis "Wie sinnvoll ist die Ergänzung des Resource Discovery Systems an der Bibliothek des Max-Planck-Instituts für evolutionäre Anthropologie durch einen zusätzlichen, externen Index?"

The dataset contains the answers that were given in the online questionnaire that was conducted as part of the Bachelor Thesis "Wie sinnvoll ist die Ergänzung des Resource Discovery Systems an der Bibliothek des Max-Planck-Instituts für evolutionäre Anthropologie durch einen zusätzlichen, externen Index?" The questionnaire and further information can be found in the Bachelor Thesis, which is linked uner Related Works.

AquaMaps: AquaMaps XML resource

from http://www.aquamaps.org/. AquaMaps are computer-generated predictions of natural occurrence of marine species, based on the environmental tolerance of a given species with respect to depth, salinity, temperature, primary productivity, and its association with sea ice or coastal areas. These __environmental envelopes__ are matched against an authority file which contains respective information for the Oceans of the World. Independent knowledge such as distribution by FAO areas or bounding boxes are used to avoid mapping species in areas that contain suitable habitat, but are not occupied by the species. Maps show the color-coded likelihood of a species to occur in a half-degree cell, with about 50 km side length near the equator. Experts are able to review, modify and approve maps.from EOL v2 database

AnAge: AnAge text (XML resource)

AnAge is a database of longevity and ageing in animals. It features quantitative life history data for over 4,000 species, including extensive longevity records, body masses at different developmental stages, reproductive data, and physiological traits related to metabolism. In addition to quantitative data, AnAge also features comments and observations related to ageing or relevant to the life history of individual taxa. AnAge features a manually-curated collection of animal longevity records. Moreover, AnAge has extensive life-history traits such as adult body weight, gestation or incubation time, age at sexual maturity and other reproductive data. Lastly, observations on physiological or pathological changes with age in animals are (where available) featured. AnAge focuses primarily on chordates. At the time of writing, AnAge features 4,122 entries, including 1,331 mammals, 1,098 birds, 539 reptiles, 169 amphibians, 962 fishes and 28 non-chordates. Our focus is on accuracy and quality, however, not quantity, and only species for which we have confidence in the data are featured. Numerous experts have contributed information to AnAge and helped us meet quality standards. Professor Steven Austad, a world-renowned expert in mammalian ageing at the Barshop Institute in San Antonio, is AnAge__s expert mammalogist and curator.

List of Digital Resources for Medieval Studies in German-speaking Countries

The Middle Ages have been brought into the digital era through the extensive digitisation of medieval source material, transforming the scope and breath of medieval studies. The field has long embraced digital methodologies, with medievalists praised as early adopters since the 1940s. German-speaking countries have brought forward innovative projects ranging from digital editions, interactive maps, to comprehensive databases and computational analysis. The following pages present a curated list of 164 digital resources and repositories for medieval studies that are initiated or located in Germany, Austria, and Switzerland. The list is not comprehensive and ongoing. It was made to aid scholars in navigating the dynamic landscape of digital medieval studies, promoting collaboration and providing insight into the field's development. The selected projects are supportive resources dealing with the Middle Ages (up to 1500) and have a clear research goal. It covers diverse topics like numerology, heraldry, monastic life, and architecture. Most of the listed digital resources are freely accessible. The projects are classified by their digital methods, as described in more detail on a separate page. The descriptors and types used to describe the projects distinctly highlight the interdisciplinary nature of medieval studies in the digital age. This list emphasises the transformative potential of digital resources, offering a starting point for continued exploration and expansion in the evolving landscape of digital medieval studies. This repository is created within the sub project <a href="https://www.virtuelle-lebenswelten.de/forschung/wissen" target="_blank" rel="nofollow noreferrer noopener">B02 Virtuelles Mittelalter</a>, curated by Juliet Diekkämper with the help of Suzette van Haaren. Sources gathered in 2023-2024.

opencc-by-4.0Sep 2024View details →

WaivOps EDM-HSE: Open Audio Resources for Machine Learning in Music

EDM-HSE Dataset EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models. Overview The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities. The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing. Specifications <ul> <li>8,000 audio loops (approximately 17 hours)</li> <li>16-bit WAV format</li> <li>Tempo range: 120–130 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Big room, electro, minimal, classic)</li> </ul> A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences. License This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance. The EDM-HSE dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>. Additional Info Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects. For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-HSE">GitHub repository</a>.

Resources for BMF CP76 RV2

These are resources for completing the BMF CP76 entitled Synergy to Combat Food Insecurity: School Meals Program and Food Banks Linkage

WaivOps WRLD-SMB: Open Audio Resources for Machine Learning in Music

WRLD-SMB Dataset WRLD-SMB is an open audio dataset featuring a collection of synthetic drum recordings in the style of Brazilian samba music. It includes 1,100 audio loops recorded in uncompressed stereo WAV format, along with paired JSON files intended for the supervised training of generative AI audio models. Overview This dataset was developed using multi-velocity audio samples and a paired MIDI dataset. The intended use of this dataset is to train or fine-tune AI models in learning high-performance drum notations, aiming to replicate the live sound of a small drum ensemble. To facilitate augmentation and supervised training with labeled audio data, a dropout technique was employed on the rendered audio files to generate variational mixes of the drum tracks. The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing. Specifications <ul> <li>1,100 audio loops (approximately 5.5 hours)</li> <li>16-bit 44.1kHz WAV format</li> <li>Tempo range: 90–120 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Traditional and modern samba, bossa nova, fusion)</li> </ul> A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences. License This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance. The WRLD-SMB dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>. Additional Info For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-WRLD-SMB">GitHub repository</a>.

Data from "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers"

This is a compilation of datasets that were used for the publication entitled "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers" by Eckehard G. BROCKERHOFF, Stephanie L. SOPOW, and Martin K.-F. BADER, published in the Journal of Applied Ecology, 'in press' in October 2024. Note: The date format is either (i) season (spring/summer/autumn/winter) plus a two-figure short form for the year (e.g., "autumn08" stands for autumn 2008), or (ii) just the year for an annual total in either four- or two-figure form in the file name (e.g., "reg2010sums.csv" or "reg10sums.csv" for the year 2010). 1. File "mean_trap_catches.csv" = Data used for Fig. 1 - Mean trap catch data of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus over time in Kaingaroa forest stands 378 ("F2006"), 377 ("F2009"), and 383 ("F2010"). For further explanations see methods of Brockerhoff et al. (2024). 2. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024). 3. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024). 4. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024). 5. File "reg10sums.csv" = Data used for Fig. 3 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024). 6. File "reg11sums.csv" = Data used for Fig. 3 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024). 7. File "reg12sums.csv" = Data used for Fig. 3 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024). 8. File "hylu2010-fitted_dispersal_to_5km-Version_23May2024.csv" = Data shown in Fig. 4 - Extension of the prediction range to 5 km of Hylurgus ligniperda dispersal data, using a generalised additive mixed model (GAMM) with beta distributed errors and the default logarithmic link. For details see caption of Fig. 4 and methods in Brockerhoff et al. (2024).

opencc-by-4.0Jul 2024View details →

SHEERM: Sustainable Household Energy and Environment Resources Management dataset

This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters.  The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation. Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data. The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.

WaivOps SYN-SE1: Open Audio Resources for Machine Learning in Music

<div> SYN-SE1 Dataset SYN-SE1 is an open audio dataset containing archived recordings of a Studio Electronics SE1 analog synthesizer. It includes 1,000 one-shot audio samples recorded in uncompressed stereo WAV format, labeled by note key across a two-octave range. The presets encompass a variety of distinct synth bass and lower-pitched lead sounds, featuring filter modulations and spatial stereo imaging, providing a valuable resource for soundfont design, audio production, and training data for generative AI models. The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include pitch detection, musical note classification, audio synthesis, music information retrieval (MIR), sound design, and signal processing. Specifications <ul> <li>1,000 audio samples</li> <li>16-bit WAV format</li> <li>Key note labeled samples</li> <li>JSON reference</li> <li>Analog synth bass and leads</li> </ul> License This dataset was compiled by WaivOps, a crowdsourced music project managed and published by Patchbanks. All recordings have been obtained from verified sources to ensure copyright clearance. The SYN-SE1 dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">(CC BY 4.0)</a>. Additional Info For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-SYN-SE1" target="_blank" rel="noopener">GitHub repository</a>. </div>

Genome data and resources on the recombination landscape and population history of the harlequin fly

This dataset contains phased vcf files of Chironomus riparius, ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files.  For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a> <ul> <li>phased-vcfs: Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC. <ul> <li>Hesse in Germany =  MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li>multihetsep-files: Created with msmc-tools. Input files for MSMC2. </li> <li>RepeatMasker: Raw output of RepeatMasker run. Summary file and file with filtered Cla-element (a transposable element) included. </li> <li>MELT: MELT ouput with added info on population and numbered insertions reflecting all 441 detected Cla insertions. </li> <li>RepeatOBserver: Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. </li> <li>MSMC2: Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. </li> <li>iSMC: Recombination rate rho in 10 kb windows and 100 kb windows along the genome. </li> <li>bedtools closest ismc 10 kb: Bedtools closest analysis of the distance of the next Cla-element to the recombination rate rho in 10 kb windows. </li> <li>bedtools closest ismc 100 kb: Bedtools closest analysis of the distance of the next Cla-element to the recombination rate rho in 100 kb windows.</li> <li>input-files figures: Supporting files needed to create figures. </li> </ul>

opencc-by-4.0Nov 2024View details →

WaivOps POP-ROK: Open Audio Resources for Machine Learning in Music

<div> POP-ROK Dataset POP-ROK is an open audio dataset featuring an uncurated collection of synthetic drum recordings in the style of pop rock music. It includes 5,378 audio loops recorded in uncompressed stereo WAV format, along with paired JSON files intended for the supervised training of generative AI audio models. Overview The POP-ROK Dataset was developed by sonifying a collection of approximately 30 acoustic drum kits with a paired MIDI dataset covering basic rhythm patterns, excluding toms. Data augmentation included a random drum-swapping method to generate unique drum kits and reverb simulations to represent various room sizes. This dataset is intended for training or fine-tuning AI models in rhythm notation with paired drum note labels, aiming to replicate the sound of live drumming. The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing. Specifications <ul> <li>5,378 audio loops (approximately 24 hours)</li> <li>16-bit WAV format</li> <li>Tempo range: 100-130 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Pop, classic rock, soft rock, country)</li> </ul> A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences. License This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance. The POP-ROK dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">(CC BY 4.0)</a>. Additional Info For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-POP-ROK" target="_blank" rel="noopener">GitHub repository</a>. </div>

openbsd-3-clauseJul 2021View details →

danbing-tk v1.3 resources

New RPGG and VNTR coordinates on 35 HGSVC genomes. Theses include: 1. pan.tr.mbe.v2.bed.gz: VNTR coordinates of the 35 HGSVC genomes over 80,518 loci. 2. pan.tr.mbe.v2.README: File format description for pan.tr.mbe.v2.bed.gz. 3. RPGG.tar.gz: The repeat-pangenome graph built from the VNTR annotations. Original manuscript describing the methods: <a href="https://www.nature.com/articles/s41467-021-24378-0">https://www.nature.com/articles/s41467-021-24378-0</a>

opencc-by-4.0Sep 2021View details →

Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland"

Extended data for "TeenCovidLife:  A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland" Wellcome Open Research submission