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.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “digital database”
Biblioshiny Report "Technolog* Digital Economy", database WoS (2000-2023)
<p>Bibliometric analysis of the WoS scientometric database data for the query "Technolog* Digital Economy". </p> <p>Filters:</p> <ul> <li>The country of Russia and publications in Russian were excluded.</li> <li>The year 2024 excluded</li> <li>Document types: Article </li> <li>Research areas: Business Economics or Public Administration or Social Sciences (Other topics)</li> <li>open access</li> </ul> <p>via <strong>Biblioshiny app</strong> </p>
PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions
<p>The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.<br><br>PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:<br><br></p> <ul> <li><strong>metadata.csv:</strong> <br>This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li><strong>Image name: </strong>image name with extension,</li> <li><strong>ECG ID:</strong> this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the <em>leads.npz </em>and <em>rhythms.npz,</em></li> <li><strong>Image relative path: </strong>relative path to the image in question,</li> <li><strong>Image page: </strong>page number of the particular image (starting from 0),</li> <li><strong>ECG number of pages: </strong>number of pages in the whole ECG,</li> <li><strong>ECG number of columns per page: </strong>number of columns per page in the ECG,</li> <li><strong>ECG number of rows per page: </strong>number of rows in the ECG,</li> <li><strong>ECG number of rhythm leads: </strong>number of rhythms in the ECG,</li> <li><strong>ECG format: </strong>short version of the ECG format.</li> </ul> </li> <li><strong>data </strong>folder: <ul> <li><strong>leads.npz: </strong>NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li><strong>rhythms.npz:</strong> NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li><strong>visual_data</strong> folder: <br>This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The <em>metadata.csv</em> file provides a direct link to each image and its associated ECG information.</li> </ul>
A Biomarker Database to Investigate Blood-Based and Digital Biomarkers in Participants Screened for Alzheimer's Disease (Bio-Hermes)
ClinicalTrials.gov study NCT04733989. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
Database of Geographic Information: Hill shade, of the 1:250000 scale Digital Elevation Model of Arizona
This data set is a hill shade, of the 1:250000 scale Digital Elevation Model of Arizona. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). A 30-minute DEM (2- by 2-arc second data spacing) consists of four 15-by 15-minute DEM blocks. Two 30-minute DEM's provide the same coverage as a standard USGS 30- by 60-minute quadrangle. Saleable units are 30- by 30-minute blocks, that is, four 15- by 15-minute DEM's representing one half of a 1:100,000-scale map.
Code and Data for Digitization and validation of a chemical synthesis literature database in the ChemPU
<p>Code and data associated with paper. Code can be used for non commerical and academic use. </p>
Scientometric Assessment of Digital Library Research Publications during 2008-2017 using SCOPUS Database
Open the record for dataset details and reuse information.
The survey of the Marega Collection at the Vatican Library and the construction of a digital open access database (マリオ・マレガ収集資料の調査とデータベース)
<p>Paper presented on Friday 11 June 2021 at the Digital Medievalist Global Symposium <em>The past, present, and future of Digital Medieval Studies</em> for the Asia & Oceania Panel, in the session Reading Indic and Japanese scripts.</p> <p>In 2011, about 14,000 documents related to the ban of Christianity in Japan and the surveillance over the family of former Christians were found at the Vatican Library. The documents, roughly spanning from the 17th to the 19th century, were originally collected by Father Mario Marega, a Salesian missionary, who resided in the Oita Prefecture, Kyushu, Japan, since 1929. Marega then sent the documents to the Vatican Library in the 1950s, where, for various reasons, they were set aside and forgotten until their rediscovery during the Library’s renovation works in 2010. Researchers from Italy and Japan have conducted research for a decade to catalogue and publish the collection, recently also made available in a digital database. In this presentation, we will introduce the surveying method and the guiding principles of the database structure and its data model of the <a href="https://base1.nijl.ac.jp/~marega/en/">Mario Marega Archive</a>. We will also elaborate on the results achieved and the issues that the research process and the database definition presented.</p> <p>2011年にバチカン図書館で17ー19世紀の日本におけるキリスト教統制に関する文書(約14,000点)が発見された。1929年より日本の大分県に滞在したイタリアのマリオ・マレガ神父が収集し、1950年代にバチカン図書館へ送ったものである。これらを研究・公開するため、イタリア・日本の研究者が共同で調査およびデータベース構築を行っている。今回の発表では、調査の方法やデータベースの考え方などを紹介し、その成果と課題を展望する。</p> <p> </p> <p> </p> <p> </p>
Figure 1 from: Kempa M, Edmondson J, Lack HW, Smatanová J, Marhold K (2016) František Nábělek's Iter Turcico-Persicum 1909–1910 – database and digitized herbarium collection. PhytoKeys 75: 69-79. https://doi.org/10.3897/phytokeys.75.9780
Figure 1 - František Nábělek during his travels in 1909–1910 (photo courtesy of family Nábělek).
Figure 2 from: Kempa M, Edmondson J, Lack HW, Smatanová J, Marhold K (2016) František Nábělek's Iter Turcico-Persicum 1909–1910 – database and digitized herbarium collection. PhytoKeys 75: 69-79. https://doi.org/10.3897/phytokeys.75.9780
Figure 2 - Schematic presentation of Nábělek's Iter Turcico-Persicum.
Development of a Database to Investigate Digital and Blood-Based Biomarkers and Their Relationship to Tau and Amyloid PET Imaging in Older Participants Who Are Cognitively Normal (CN), Have Mild Cogni
ClinicalTrials.gov study NCT06584357. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
Database of Positon Emission Tomography (PET) Digital Brain Exams, in 50th Age of Patients
ClinicalTrials.gov study NCT04163276. IPD Sharing: NO. Countries: 1. Publications: 0.
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.