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.
354
datasets available to search
ShareScore release 0.9.0
Dataset results
354 results for “data access”
Expression data of 8 rice accessions under cold stress in seedling stage
GEO Series GSE71680. Oryza sativa. 24 samples. Type: Expression profiling by array.
Precipitation Measurement Missions Data Access
Tropical Rainfall Measuring Mission (TRMM) data products are currently available from 1998 to the present. Global Precipitation Measurement (GPM) mission data products are currently available from March 2014 to the present. TRMM and GPM are joint missions between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study global precipitation. Here we outline the types of products currently available for TRMM and GPM and the various ways to query, view and download these precipitation products.
Precipitation Measurement Missions Data Access
Tropical Rainfall Measuring Mission (TRMM) data products are currently available from 1998 to the present. Global Precipitation Measurement (GPM) mission data products are currently available from March 2014 to the present. TRMM and GPM are joint missions between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study global precipitation. Here we outline the types of products currently available for TRMM and GPM and the various ways to query, view and download these precipitation products.
Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity (ATAC-Seq data).
GEO Series GSE195623. Homo sapiens. 45 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A dataset of Data Subject Access Request Packages
<h3>Overview</h3> <p>This dataset is a minimal example of Data Subject Access Request Packages (SARPs), as they can be retrieved under data protection laws, specifically the GDPR. It includes data from two data subjects, each with accounts for five major sevices, namely Amazon, Apple, Facebook, Google, and Linkedin.</p> <p> </p> <h3>Purpose and Usage</h3> <p>This dataset is meant to be an initial dataset that allows for manual exploration of structures and contents found in SARPs. Hence, the number of controllers and user profiles should be minimal but sufficient to allow cross-subject and cross-controller analysis. This dataset can be used to explore structures, formats and data types found in real-world SARPs. Thereby, the planning of future SARP-based research projects and studies shall be facilitated.<br><br>We invite other researchers to use this dataset to explore the structure of SARPs. The envisioned primary usage includes the development of user-centric privacy interfaces and other technical contributions in the area of data access rights. Moreover, these packages can also be used for examplified data analyses, although no substantive research questions can be answered using this data. In particular, this data does not reflect how data subjects behave in real world. However, it is representative enough to give a first impression on the types of data analysis possible when using real world data. </p> <h3> </h3> <h3>Data Generation </h3> <p>In order to allow cross-subject analysis, while keeping the re-identification risk minimal, we used research-only accounts for the data generation. A detailed explanation of the data generation method can be found in the paper corresponding to the dataset, accepted for the Annual Privacy Forum 2024.</p> <p>In short, two user profiles were designed and corresponding accounts were created for each of the five services. Then, those accounts were used for two to four month. During the usage period, we minimized the amount of identifying data and also avoided interactions with data subjects not part of this research. Afterwards, we performed a data access request via the controller's web interface. Finally, the data was cleansed as described in detail in the acconpanying paper and in brief within the following section.</p> <h3> </h3> <h3>Data Cleansing</h3> <p>Before publication, both possibly identifying information and security relevant attributes need to be obfuscated or deleted. Moreover, multi-party data (especially messages with external entities) must be deleted. If data is obfuscated, we made sure to substitute multiple occurances of the same information with the same replacement.<br>We provide a list of deleted and obfuscated items, the obfuscation scheme and, if applicable, the replacement.</p> <p>The list of obfuscated items looks like the following example:</p> <table> <tbody> <tr> <td>path</td> <td>filetype</td> <td>filename</td> <td>attribute</td> <td>scheme</td> <td>replacement</td> </tr> <tr> <td>linkedin\Linkedin_Basic</td> <td>csv</td> <td>messages.csv</td> <td>TO</td> <td>semantic description</td> <td>Firstname Lastname</td> </tr> <tr> <td>gooogle\Meine Aktivitäten\Datenexport</td> <td>html</td> <td>MeineAktivitäten.html</td> <td>IP Address</td> <td>loopback</td> <td>127.142.201.194</td> </tr> <tr> <td>facebook\personal_information</td> <td>json</td> <td>profile_information.json</td> <td>emails</td> <td>semantic description</td> <td>firstname.lastname@gmail.com</td> </tr> </tbody> </table> <h3> </h3> <h3>Data Characterization</h3> <p>To give you an overview of the dataset, we publicly provide some meta-data about the usage time and SARP characteristics of exports from subject A/ subject B.</p> <table> <tbody> <tr> <td>provider</td> <td>usage time<br>(in month)</td> <td>export options</td> <td>file types</td> <td># subfolders</td> <td># files</td> <td>export size</td> </tr> <tr> <td>Amazon</td> <td>2/4</td> <td>all categories</td> <td>CSV (32/49)<br>EML (2/5)<br>JPEG (1/2)<br>JSON (3/3)<br>PDF (9/10)<br>TXT (4/4)</td> <td>41/49</td> <td>51/73</td> <td>1.2 MB / 1.4 MB</td> </tr> <tr> <td>Apple</td> <td>2/4</td> <td>all data<br>max. 1 GB/ max. 4 GB</td> <td>CSV (8/3)</td> <td>20/1</td> <td>8/3</td> <td>71.8 KB / 294.8 KB</td> </tr> <tr> <td>Facebook</td> <td>2/4</td> <td> <p>all data</p> <p>JSON/HTML</p> <p>on my computer</p> </td> <td>JSON (39/0)<br>HTML (0/63)<br>TXT (29/28)<br>JPG (0/4)<br>PNG (1/15)<br>GIF (7/7)</td> <td>45/76</td> <td>76/117</td> <td>12.3 MB / 13.5 MB</td> </tr> <tr> <td>Google</td> <td>2/4</td> <td> <p>all data</p> <p>frequency once</p> <p>ZIP</p> <p>max. 4 GB</p> </td> <td>HTML (8/11)<br>CSV (10/13)<br>JSON (27/28)<br>TXT (14/14)<br>PDF (1/1)<br>MBOX (1/1)<br>VCF (1/0)<br>ICS (1/0)<br>README (1/1)<br>JPG (0/2)</td> <td>44/51</td> <td>64/71</td> <td>1.54 MB /1.2 MB</td> </tr> <tr> <td>LinkedIn</td> <td>2/4</td> <td>all data</td> <td>CSV (18/21)</td> <td>0/0 (part 1/2)<br>0/0 (part 1/2)</td> <td>13/18<br>19/21</td> <td> <p>3.9 KB / 6.0 KB</p> <p>6.2 KB / 9.2 KB</p> </td> </tr> </tbody> </table> <h3><br>Authors</h3> <p>This data collection was performed by Daniela Pöhn (Universität der Bundeswehr München, Germany), Frank Pallas and Nicola Leschke (Paris Lodron Universität Salzburg, Austria). For questions, please contact nicola.leschke@plus.ac.at.</p> <h3>Accompanying Paper</h3> <p>The dataset was collected according to the method presented in:<br>Leschke, Pöhn, and Pallas (2024). "How to Drill Into Silos: Creating a Free-to-Use Dataset of Data Subject Access Packages". Accepted for Annual Privacy Forum 2024.</p>
SocketSense Open Access Data
<p>This is the summary of SocketSense's open access data.</p> <p><strong>SocketSense (https://www.socketsense.eu/) </strong>is a<strong> EU funded </strong>project aimed to develop an innovative advanced sensor-based socket system that will enable comfortable socket manufacturing tailored to patients needs. The cutting-edge technology will use real-time monitoring of residual limb tissues evolvement by collecting data through advanced embedded sensors. The end product will be designed through biomechanical modelling and CAD/CAM tools and finally coming to life via additive manufacturing.<br>With SocketSense, the prosthetists will be able to achieve a good-fit socket within the same day when the patient needs a new one, and the technique will apply to all lower limb amputees (above knee and below knee).</p> <p>Publications list</p> <pre>1. <a href="https://doi.org/10.1109/biocas54905.2022.9948616">Wearable pressure sensing for lower limb amputees.</a> 2. <a href="https://isb2021.com/">Fuzzy-logic Inference System for Transfemoral Socket Rectification.</a> 3. <a href="https://isb2021.com/">Biomechanical response of residual limb: combining shear-wave elastography and finite element analysis. </a> 4. <a href="https://isb2021.com/">Ultrasound investigation of muscle size and muscle properties in transfemoral amputees. </a> 5. <a href="https://doi.org/10.1109/DSD53832.2021.00038">Evaluation of Time Series Clustering on Embedded Sensor Platform. </a> 6. <a href="https://doi.org/10.3390/s21155016">A scoping review of pressure measurements in prosthetic sockets of transfemoral amputees during ambulation: key considerations for sensor design.</a> 7.<a href="https://doi.org/10.3390/s21113764"> Development of Prototype Low Cost QTSS™ Wearable Flexible more Envirofriendly Pressure, Shear and Friction sensors for dynamic Prosthetic Fit Monitoring. </a> 8. <a href="https://doi.org/10.3390/s21113743">A Sensor-based Decision Support System for Transfemoral Socket Rectification.</a> 9. <a href="https://doi.org/10.5220/0010838600003123">A Mechatronics-Twin Framework based on Stewart Platform for Effective Exploration of Operational Behaviors of Prosthetic Sockets with Amputees. </a> 10. <a href="https://doi.org/10.3390/s22093103">Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study.</a> 11. <a href="https://www.ot-world.com/index-en.html">Simultaneous intra-socket shear and pressure measurements of a transfemoral amputee during dynamic and static activities: a feasibility study. </a> 12. <a href="https://www.ot-world.com/index-en.html">A toolbox for bi-directional conversions between 3D prosthetic socket stress measurements and its representations in 2D. </a> 13.<a href="https://doi.org/10.3390/app12030986"> Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with A Mechatronics-Twin Framework.</a> 14. <a href="https://www.diva-portal.org/smash/get/diva2:1371201/FULLTEXT01.pdf">Master Thesis: Wearable sensors in prosthetic socket. </a> 15. <a href="http://kth.diva-portal.org/smash/get/diva2:1524817/FULLTEXT01.pdf">Master Thesis: Effective Optimization of Deployment for Wearable Sensors in Transfemoral Prosthesis. </a> </pre> <p> </p> <p>Datasets list</p> <p><br>1. <a href="https://www.mdpi.com/1424-8220/21/11/3764">RISE_DS01_Development of Prototype Low-Cost QTSS™ Wearable Flexible More Enviro-Friendly Pressure, Shear, and Friction Sensors for Dynamic Prosthetic Fit Monitoring </a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link: https://www.mdpi.com/1424-8220/21/11/3764 <br>D.O.I linked Publication: https://www.mdpi.com/1424-8220/21/11/3764<br>2. <a href="https://www.socketsense.eu/wp-content/uploads/sites/50/2021/04/White-Paper-sensors-Nov-2020-v2.pdf">RISE_DS02_Whitepaper on sensors</a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link: https://www.socketsense.eu/wp-content/uploads/sites/50/2021/04/White-Paper-sensors-Nov-2020-v2.pdf<br>D.O.I linked Publication: https://www.socketsense.eu/wp-content/uploads/sites/50/2021/04/White-Paper-sensors-Nov-2020-v2.pdf<br>3. <a href="https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo">SAS_DS01_Sensor measurements</a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link (Limited access): https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo<br>4. <a href="https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo">SAS_DS02_Scales outcomes</a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link (Limited access): https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo<br>5. <a href="https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo">SAS_DS03_Ultrasound and elastography</a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link (Limited access): https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/hhuuvr/innovacion/GIT/repo<br>6.<a href="https://zenodo.org/record/7624740#.Y-UeuHbP1jE"> Össur_DS01_PilotStudy</a><br>SocketSense Össur DS01 Pilot Study<br>DOI: 10.5281/zenodo.7624740<br>Repository link: https://zenodo.org/record/7624740#.Y-UeuHbP1jE<br>7. <a href="https://zenodo.org/record/7624975#.Y-UvknbP1jE">Össur_DS01_PilotStudy_Shear+Pressure</a><br>SocketSense Össur DS01 Pilot Study Shear & Pressure<br>DOI: 10.5281/zenodo.7624975<br>Repository link: https://zenodo.org/record/7624975#.Y-UvknbP1jE<br>8. <a href="http://https://zenodo.org/record/7625218#.Y-UedXbP1jE">Össur_DS02_2DResidualLimbMaps</a><br>SocketSense Össur DS02 2D Residual Limb Maps<br>DOI: 10.5281/zenodo.7625218<br>Repository link: https://zenodo.org/record/7625218#.Y-UedXbP1jE<br>9. <a href="https://www.southtees.nhs.uk/about/strive/innovation-team/what-innovation-does/ ">STH_DS01_Patient data </a><br>DOI: 10.5281/zenodo.7400478 (This summary repository)<br>Repository link (Limited access): https://www.southtees.nhs.uk/about/strive/innovation-team/what-innovation-does/ <br>10.<a href="https://zenodo.org/record/7615573#.Y-JfdHbMLSE"> KTH_DS01_Sensor Redundancy Reduction</a><br>The program involved in the 2022 MDPI Sensors paper “Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study”.<br>DOI: D.O.I: 10.5281/zenodo.7615573<br>Repository link: https://zenodo.org/record/7615573#.Y-JfdHbMLSE<br>11. <a href="https://zenodo.org/record/7615573#.Y-JfdHbMLSE">KTH_DS02_Sensor Clustering</a><br>The program involved in the 2021 24th Euromicro Conference on Digital System Designpaper “Evaluation of Time Series Clustering on Embedded Sensor Platform”.<br>DOI: 10.5281/zenodo.7615573<br>Repository link: https://zenodo.org/record/7615573#.Y-JfdHbMLSE<br>12. <a href="https://zenodo.org/record/7713030#.ZAn6mdLMIUE">KTH_DS03_Stewart_Platform_Mock_Trials</a><br>DOI: 10.5281/zenodo.7713030<br>Repository link: https://zenodo.org/record/7713030#.ZAn6mdLMIUE<br>13. <a href="https://zenodo.org/record/7713030#.ZAn6mdLMIUE">KTH_DS04_Biomechanical_Model</a><br>DOI: 10.5281/zenodo.7713030<br>Repository link: https://zenodo.org/record/7713030#.ZAn6mdLMIUE<br>14.<a href="https://zenodo.org/record/7713030#.ZAn6mdLMIUE"> KTH_DS05_Finite_Element_Analysis</a><br>DOI: 10.5281/zenodo.7713030<br>Repository link: https://zenodo.org/record/7713030#.ZAn6mdLMIUE<br>15. <a href="https://zenodo.org/record/7656763#.Y_NartLMIUE ">TWI_DS01_Samples of Rectified Transfemoral Sockets with Fuzzy-Logic-Based Decision Support System</a><br>This dataset contains sample rectified transfemoral sockets as an output of the fuzzy-logic DSS.<br>DOI: 10.5281/zenodo.7656763<br>Repository link: https://zenodo.org/record/7656763#.Y_NartLMIUE <br>D.O.I linked Publication: https://doi.org/10.3390/s21113743</p> <p><br> </p> <p> </p> <p> </p> <p> </p>
Expression data from female SD rats with access to lifelong exercise
GEO Series GSE5085. Rattus norvegicus. 12 samples. Type: Expression profiling by array.
Expression data from torpedo-stage embryos from different Arabidopsis accessions
GEO Series GSE47884. Arabidopsis thaliana. 11 samples. Type: Expression profiling by array.
Raw data of accessions used for crossings in T5.5
<p>Raw data of buckwheat accessions used for crossings in T5.5. - the first season.</p>
SCAD-zbMATH-01 Open Access Data Set for Author Name Disambiguation (AND)
<p> </p> <p><strong>Note: This data set is <em>deprecated</em>. Please use the enhanced, open access version available at https://doi.org/10.5281/zenodo.161333</strong><strong> !</strong></p> <p> </p> <p>This data set contains disambiguated publication data from zbMATH (www.zbmath.org) for use in author name disambiguation (AND). </p> <p>It covers 28321 publications with 33810 authorship records, authored by 2946 distinct authors. Authorship records have been manually annotated with author identifiers. </p> <p>For details, see "Data Sets for Author Name Disambiguation: An Empirical Analysis and a New Resource", Mark-Christoph Müller, Florian Reitz, and Nicolas Roy, 2016, submitted to Scientometrics.</p>
Expression data of 20 Arabidopsis thaliana accessions at dusk, dawn and after an extension of the night
GEO Series GSE71188. Arabidopsis thaliana. 119 samples. Type: Expression profiling by array.
Stem trichome PARE-Seq (degradome) data from the 20 accessions
<p><strong>PARE-Seq / degradome dataset</strong></p> <p>Obtained from 10µg of total RNA isolated from stem trichomes of cultivated (S. lycopersicum) and wild relatives of tomato (Solanum section Lycopersicon). </p> <p>Degradome sequencing (sequencing of 5' end of uncapped mRNAs --> to find the site of microRNA cleavage).<br> Degradome sequencing is also called PARE-Seq (see German et al., Nature Protocols 2009 4(3):356-62). Here, Vertis Biotech AG used a slightly modified protocol that generates 75nt reads from the 5' uncapped end of the mRNA. </p> <p><strong>Sample description</strong><br> Stem trichomes total RNA from several individual plants of the "20 accessions" (see table of genotypes below). See lab book - Marc Galland lab book #4 (2017): pages 61-69 + 73-75.</p> <p><strong>Protocol used</strong><br> Qiagen RNeasy Plant Mini Kit (Cat No./ID: 74904).<br> DNAse treatment by Vertis. <br> Minimal amount of 10µg of total RNA per sample.</p> <p><strong>Sequencing at Vertis Biotech AG (Germany)</strong><br> See Doc_VB1745_Galland.pdf and Doc_VB1745_2_Galland.pdf together with the two SeqData08.08.2017.pdf and SeqData08.</p> <p>The sequencing run has generated a total of 20 fastq files: </p> <ol> <li>Moneymaker_C32_S3_R1_001.fastqc.gz</li> <li>LA0407_S10_R1_001.fastq.gz</li> <li>LA0716_S29_R1_001.fastq.gz</li> <li>LA1278_S27_R1_001.fastq.gz</li> <li>LA1364_S8_R1_001.fastq.gz</li> <li>LA1401_S5_R1_001.fastq.gz</li> <li>LA1578_S26_R1_001.fastq.gz</li> <li>LA1718_S4_R1_001.fastq.gz</li> <li>LA1777_S7_R1_001.fastq.gz</li> <li>LA1840_S6_R1_001.fastq.gz</li> <li>LA1954_S28_R1_001.fastq.gz</li> <li>LA2133_S9_R1_001.fastq.gz</li> <li>LA2172_S11_R1_001.fastq.gz</li> <li>LA2386_S21_R1_001.fastq.gz</li> <li>LA2695_S20_R1_001.fastq.gz</li> <li>LA4024_S26_R1_001.fastq.gz</li> <li>LA0735_LYC140_S24_R1_001.fastq.gz</li> <li>PI134418_LYC38_S23_R1_001.fastq.gz</li> <li>LYC4_S25_R1_001.fastq.gz</li> <li>PI127826_S22_R1_001.fastq.gz</li> </ol> <p><strong>Table of genotypes used</strong></p> <pre><code class="language-markdown">| accession | species | accession nr | synonym | origin | |------------|--------------------------------|--------------|---------|-------------| | LA2172 | S. arcanum | TR0009 | - | Peru | | LA1401* | S. cheesmaniae f. minor | EA00652 | - | Ecuador | | LA1840 | S. chmielewskii | - | - | unknown | | LA2695 | S. chmielewskii | EA00759 | - | Peru | | LA0407 | S. habrochaites f. glabratum | EA00558 | - | Ecuador | | LA1777 | S. habrochaites f. hirsutum | EA00703 | - | Peru | | PI134418 | S. habrochaites f. glabratum | TR00015 | LYC38 | unknown | | LYC4 | S. habrochaites f. hirsutum | TR00017 | - | unknown | | LA1718 | S. habrochaites f. glabratum | EA00699 | LYC4934 | Peru | | PI127826 | S. habrochaites f. hirsutum | - | - | Peru | | LA1364 | S. huaylasense | TR00030 | - | Peru | | Moneymaker | S. lycopersicum | - | C32 | Netherlands | | LA4024 | S. lycopersicum | TA209 | - | unknown | | LA2133 | S. neorickii/L. parviflorum | EA00729 | - | Peru | | LA0735 | S. neorickii | TR00025 | LYC140 | unknown | | LA0716 | S. pennellii | EA00585 | - | Peru | | LA1278 | S. peruvianum/pimpinellifolium | TR00005 | - | unknown | | LA1954 | S. peruvianum | EA00713 | - | Peru | | LA1578 | S. pimpinellifolium | EA00674 | - | Peru |</code></pre> <p> </p> <p><strong>Reference:</strong></p> <p>German et al., Nature Protocols 2009 4(3):356-62)</p>
Restricted access data for the paper Fake News on Twitter During the 2016 U.S. Presidential Election
<p>Restricted access data for replicating results in the paper Fake News on Twitter During the 2016 U.S. Presidential Election.</p>
Digital Accessibility of Life Science Data Portals and Journal Websites
<p>Enhancing the diversity and inclusion of the life sciences workforce has become an important problem as highlighted by many organizations in the US, including NIH, NHGRI, and NSF. People with visual impairments are one of the groups that face barriers to access to the biology workforce. To overcome this challenge, it is important to understand their current barriers in biological research and education. The most common assistive technology used by people with visual impairments is the screen reader (45.2%). However, multiple studies found that many websites largely fail to meet accessibility guidelines, making it challenging or even impossible for screen reader users to access existing resources. To help gain better insights into how well people with visual impairments can access existing biological resources, we evaluated the digital accessibility of two essential resources for data-driven studies—data portals and journal websites. Using an automated evaluation tool, we collected accessibility evaluation data for a large corpus of resources (<i>N</i>=3,943). In addition, we collected metadata of individual resources (e.g., geospatial, temporal, and impact score data) for a more insightful analysis. All datasets, as well as the entire source code, are available online on Zenodo and GitHub under a CC-BY and MIT license, respectively.</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.