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.
893
datasets available to search
ShareScore release 0.9.0
Dataset results
893 results for “Relational analysis”
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration
<p>Summary statisics of an association study of 6,908,005 genetic variants with anti-VEGF nAMD treatment response in 179 treatment-naïve nAMD probands. This dataset supplements the publication "Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration" (DOI: 10.3390/ijms23116094). Details regarding the methods and version numbers can be found in the corresponding manuscript.</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
[Dataset] Analysis of ego-networks of two CS-related Twitter accounts
<p><strong>Explanation/Overview:</strong></p> <p>This is the dataset for the analyses and results on Twitter Ego-Networks of two CS-related accounts (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>). The username have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R or any other network visualisation software (e.g., Gephi). The edges represent the follow relation and were retrieved using the Twitter API. All usernames except those of the two ego-accounts were anonymised by assigning a random number to each node. Due to the size of the network, we do not include any <code>.gexf </code>or <code>.gml </code>files in this upload, but rather resort to node and edge lists in the <code>.json</code> format</p> <p><strong>Relatedness:</strong></p> <p>The networks are the ego-networks for two related public accounts that are associated with citizen science (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>).</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, two files can be found.</p> <ul> <li><code>edges.json</code></li> </ul> <p>Represents the edge list, with the columns:</p> <table> <tbody> <tr> <td><code>source</code></td> <td><code>target</code></td> <td><code>wherefrom</code></td> <td><code>account</code></td> </tr> <tr> <td>EuCitSci</td> <td>23929</td> <td>friendslist</td> <td>EuCitSci</td> </tr> </tbody> </table> <p><code>Source</code> and <code>target</code> are the necessary columns for the network creation and in this example indicate that EuCitSci follows 23929. <code>wherefrom </code>indicates the origin of this relation in the crawling (i.e., whether it was retrieved using the friends or follower list) and <code>account </code>indicates the ego-account it belongs to. Thus, the edges can also be separated using this <code>account </code>attribute as they represent two distinct networks.</p> <ul> <li><code>nodes.json</code></li> </ul> <p>Represents the nodes in the networks. The following data fields are contained:</p> <table> <tbody> <tr> <td><code>username</code></td> <td><code>CS</code></td> <td><code>followers_count</code></td> <td><code>friends_count</code></td> <td>...</td> </tr> <tr> <td>1116</td> <td>CS</td> <td>689</td> <td>514</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>favourites_count</code></td> <td><code>listed_count</code></td> <td><code>statuses_count</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>2601</td> <td>18</td> <td>2141</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>degree</code></td> <td><code>in_degree</code></td> <td><code>out_degree</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>21</td> <td>5</td> <td>16</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>reciprocity</code></td> <td><code>account</code></td> </tr> <tr> <td>...</td> <td>0.48</td> <td>EuCitSci</td> </tr> </tbody> </table> <p> </p> <p><code>Username </code>represents the numerical and anonymised username, <code>CS</code> the community-membership. The different counts (e.g., <code>followers_count</code>) indicate the number of followers the user had at the time of the retrieval by the Twitter API. <code>degree</code> refers to the degree in the network (similarly the <code>in-</code> and <code>out_degree</code>), while <code>reciprocity</code> refers to the number of mutual edges in respect to the total number of edges per node (see <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html">here</a>). <code>Account</code> is similar as described above.</p> <p> </p> <p><strong>Grouping:</strong></p> <p>The data is grouped according the ego-account it is associated to, as can be read above (i.e., the <code>account</code> attribute).</p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Single-cell analysis of megakaryopoiesis in peripheral CD34+ cells: insights into ETV6-related thrombocytopenia
<p>This repository contains necessary files for reproducing the analysis in Bigot et al, 2023. The instructions for reproducing the analysis are given in github (https://github.com/poggiteam/ETV6_2020).</p>
A dataset for temporal analysis of files related to the JFK case
<p>This dataset contains the content of the subset of all files with a correct publication date from the 2017 release of files related to the JFK case (retrieved from https://www.archives.gov/research/jfk/2017-release). This content was extracted from the source PDF files using the R OCR libraries tesseract and pdftools.</p> <p>The code to derive the dataset is given as follows:</p> <p>### BEGIN R DATA PROCESSING SCRIPT</p> <p>library(tesseract)<br> library(pdftools)</p> <p>pdfs <- list.files("/home/STAFF/luczakma/RProjects/JFK/data/files/")</p> <p>meta <- read.csv2("/home/STAFF/luczakma/RProjects/JFK/data/jfkrelease-2017-dce65d0ec70a54d5744de17d280f3ad2.csv",header = T,sep = ',')</p> <p>meta$Doc.Date <- as.character(meta$Doc.Date)</p> <p>meta.clean <- meta[-which(meta$Doc.Date=="" | grepl("/0000",meta$Doc.Date)),]<br> for(i in 1:nrow(meta.clean)){<br> meta.clean$Doc.Date[i] <- gsub("00","01",meta.clean$Doc.Date[i])<br> <br> if(nchar(meta.clean$Doc.Date[i])<10){<br> meta.clean$Doc.Date[i]<-format(strptime(meta.clean$Doc.Date[i],format = "%d/%m/%y"),"%m/%d/%Y")<br> }<br> <br> }</p> <p>meta.clean$Doc.Date <- strptime(meta.clean$Doc.Date,format = "%m/%d/%Y")</p> <p>meta.clean <- meta.clean[order(meta.clean$Doc.Date),]</p> <p>docs <- data.frame(content=character(0),dpub=character(0),stringsAsFactors = F)<br> for(i in 1:nrow(meta.clean)){<br> #for(i in 1:3){<br> pdf_prop <- pdftools::pdf_info(paste0("/home/STAFF/luczakma/RProjects/JFK/data/files/",tolower(gsub("\\s+"," ",gsub(" ","",meta.clean$File.Name[i])))))<br> tmp_files <- c()<br> for(k in 1:pdf_prop$pages){<br> tmp_files <- c(tmp_files,paste0("/home/STAFF/luczakma/RProjects/JFK/data/tmp/",k))<br> }<br> <br> img_file <- pdftools::pdf_convert(paste0("/home/STAFF/luczakma/RProjects/JFK/data/files/",tolower(gsub("\\s+"," ",gsub(" ","",meta.clean$File.Name[i])))), format = 'tiff', pages = NULL, dpi = 700,filenames = tmp_files)<br> <br> txt <- ""<br> <br> for(j in 1:length(img_file)){<br> extract <- ocr(img_file[j], engine = tesseract("eng"))<br> #unlink(img_file)<br> txt <- paste(txt,extract,collapse = " ")<br> }<br> <br> docs <- rbind(docs,data.frame(content=iconv(tolower(gsub("\\s+"," ",gsub("[[:punct:]]|[\n]"," ",txt))),to="UTF-8"),dpub=format(meta.clean$Doc.Date[i],"%Y/%m/%d"),stringsAsFactors = F),stringsAsFactors = F)<br> }</p> <p>### END R DATA PROCESSING SCRIPT</p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Analysis of the content of the H2020 project websites related to LEAs and IA.
<p>This is the dataset used in the article entitled "The disconnect between the goals of trustworthy AI for law enforcement and the EU research agenda". You can find more information about the results obtained, as well as the methodology used in the paper.</p>
Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis
<p>Genetic studies have identified ≥240 loci associated with risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterize the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into <em>cis</em>- and <em>trans</em>-acting genetic components and mapping <em>cis</em>-acting loci associated with miRNA expression (miRNA-eQTLs). We find (i) 84 heritable miRNAs, primarily regulated by <em>trans</em>-acting genetic effects, and (ii) 5 miRNA-eQTLs. We also use several different strategies to identify T2D-associated miRNAs. First, we colocalize miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersect miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and find 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we perform differential expression analysis and identify 14 miRNAs associated with T2D status—including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p—and 4 miRNAs associated with a polygenic score for HbA1c levels—miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.</p>
TweetBLM: A Hate Speech Dataset and Analysis of BlackLivesMatter-related Microblogs on Twitter
<p>Collection of BLM related tweets and their corresponding labels of hate speech.</p>
Dataset related to article "Phantom‑based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM"
<p>The dataset comprises information from several DICOM tags extracted from digital mammography (DM), digital breast tomosynthesis (DBT), and contrast-enhanced mammography (CEM) images acquired in a phantom study aimed at characterizing the automatic exposure control (AEC) behavior of diverse mammography equipment. The final ten columns of the datasets encompass signal (mena pixel values, MPV) and noise (standard deviation, SD) measurements derived from phantom images. These measurements are used to compute several image quality metrics, including contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), CNR relative difference in comparison to the 45 mm reference thickness, and a figure of merit (FOM) obtained by diving the squared CNR by the mean glandular dose (MGD).</p>
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
Diabetes-related excess mortality in Mexico: a comparative analysis of national death registries between 2017-2019 and 2020
<p>Dataset to replicate the article "Diabetes-related excess mortality in Mexico: a comparative analysis of national death registries between 2017-2019 and 2020" available in MedRxiv at: https://www.medrxiv.org/content/10.1101/2022.02.24.22271337v1</p> <p>Code available at: https://github.com/oyaxbell/diabetes_excess</p>
Fig. 2 in Comparative Analysis Of Fecundity In Related Amphimictic Aporrectodea Caliginosa And Apomictic A. Trapezoides Earthworms, And The Problem Of Reproductive Advantages Of Parthenogenetic Animals
Fig. 2. Mean number of cocoons per one mature specimen in close parthenogenetic (A. trapezoides) and amphimictic (A. caliginosa) earthworm species during the reproduction season of 2019.
Fig. 1 in Comparative Analysis Of Fecundity In Related Amphimictic Aporrectodea Caliginosa And Apomictic A. Trapezoides Earthworms, And The Problem Of Reproductive Advantages Of Parthenogenetic Animals
Fig. 1. Mean number of cocoons per one mature specimen in close parthenogenetic (A. trapezoides) and amphimictic (A. caliginosa) earthworm species during the reproduction season of 2018.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</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.