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.
141
datasets available to search
ShareScore release 0.9.0
Dataset results
141 results for “research database”
CIBMTR Research Database
ClinicalTrials.gov study NCT01166009. IPD Sharing: NO. Countries: 1. Publications: 1.
Czech Multicentre Research Database of Chronic Obstructive Pulmonary Disease (COPD)
ClinicalTrials.gov study NCT01923051. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Neuropsychiatric Research Databases for People With Intellectual Disabilities and Epilepsy (REFINE)
ClinicalTrials.gov study NCT06780501. IPD Sharing: Not stated. Countries: 1. Publications: 1.
UroCCR Database: French Research Network for Kidney Cancer -UroCCR
ClinicalTrials.gov study NCT03293563. IPD Sharing: NO. Countries: 4. Publications: 4.
Use of the Perinatal Database and Biologic Tissue Bank at The Perinatology Research Branch (PRB)
ClinicalTrials.gov study NCT00340249. IPD Sharing: Not stated. Countries: 1. Publications: 3.
COllaboration REsearch on Chronic Cough (COREC): a French Multicenter Database
ClinicalTrials.gov study NCT04882943. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: RECIFS: A centralized geo-environmental database for coral reef research and conservation
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Data from: A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon
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Data from: A global database and ‘state of the field’ review of research into ecosystem engineering by land animals.
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The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database
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Data from: Towards an integrated database on Canadian ocean resources: benefits, current states, and research gaps
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Data from: Improper data practices erode the quality of global ecological databases and impede the progress of ecological research
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The FAIR database: facilitating access to public health research literature
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Researcher-in-the-loop for systematic reviewing of text databases
<p>Machine learning with human interaction is gaining popularity. Humans are intelligent but slow and not very accurate, whereas machines are fast and accurate but not (very) intelligent. Combining the strengths of both in interactive processes can advance the capabilities of machine learning. The idea of combining human intelligence and machine learning can be found in machine learning techniques like active learning (AL) and Human-in-the-Loop (HITL) machine learning. In AL and HITL, the human classifies unlabeled items the model selects to learn from, e.g., items for which the model is uncertain. These techniques are proven to be effective for training models.</p> <p>In most HITL machine learning applications, the interaction with the human is used to train a model with a minimum number of labeling tasks. However, there is a type of problems where the model is not the primary output, but the systematic exploration of a relevant subset of the given dataset. Usually, these data points should be all be seen, in any case, by a human. In this case, the algorithm(s) are interactively optimized for finding these data points, instead of making the model more accurate. Examples are found in applications were challenges have a unique character and deal with large volumes of data, like systematic reviewing with the goal to create an overview of scientific literature, developing medical guidelines, or police investigations. In these applications, there is also a strong systematic component as all data points need to be judged with the same inclusion criteria. Therefore, we propose the term Researcher-In-The-Loop (RITL) as a special case of HITL with three unique components: (1) The primary output of the process is a selection of the data, not a trained machine learning model, (2) All data points in the relevant selection are seen by a human at the end of the process, (3) The use-case requires a strong systematic way of working.</p> <p>In this presentation, we study a use-case of RITL machine learning about reading COVID-19 related scientific papers. With the emergence of online publishing, the number of scientific papers on any topic, but especially COVID-19, is skyrocketing. All these textual data present opportunities to scholars and practitioners, while simultaneously confronting them with new challenges like the sparsity of relevant items. For example, this challenge shows up in the development of specific medical guidelines. To develop comprehensive overviews of the relevant studies, scholars often develop systematic reviews. Such reviews entail several explicit and reproducible steps, including identifying all likely relevant publications in a standardized way, extracting data from eligible studies, and synthesizing the results. To avoid bias in systematic reviews, it is of crucial importance to find a way to effectively automatize this screening process. Therefore, we developed software combining natural language processing and active learning: ASReview: Active Learning for Systematic Reviews. The source code is available open source via GitHub. We show that by using ASReview the laborintensive task of labeling text leads to far more efficient reviewing than a full manual review.</p>
PEDs research database
<p>The accumulation of data is pivotal in constructing an all-encompassing analysis of Positive Energy Districts (PEDs), facilitating a thorough examination of their functionality and impact within urban environments. This collected information serves as the cornerstone for developing an intricate design methodology tailored specifically for PEDs. By delving deep into this data, our aim is to unearth invaluable insights crucial for formulating a methodology that not only optimizes the performance of PEDs but also paves the way for sustainable and energy-efficient urban development strategies.</p>
Supplementary material 1 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
A template for an occurrence or specimen record to be imported as a material citation.
Figure 5 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 5 - Selection of the journal and "Data Paper (Biosciences)" template in the ARPHA Writing Tool.
Figure 2 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 2 - This fictionalized workflow presents the flow of information content of biodiversity specimens or biodiversity occurrences from the data portals GBIF, BOLD Systems, iDigBio, and PlutoF, through user-interface elements in AWT to textualized content in a Taxonomic Paper manuscript template intended for publication in the Biodiversity Data Journal.
Figure 3 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 3 - User interface of the ARPHA Writing Tool controlling the import of specimen records from external databases.
Figure 6 from: Gerovasileiou V, Martínez A, Álvarez F, Boxshall G, Humphreys W, Jaume D, Becking L, Muricy G, van Hengstum P, Dekeyzer S, Decock W, Vanhoorne B, Vandepitte L, Bailly N, Iliffe T (2016) World Register of marine Cave Species (WoRCS): a new Thematic Species Database for marine and anchialine cave biodiversity. Research Ideas and Outcomes 2: e10451. https://doi.org/10.3897/rio.2.e10451
Figure 6 - WoRCS also includes images showing the significance of some cave species for local societies.
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.