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.
193
datasets available to search
ShareScore release 0.9.0
Dataset results
193 results for “Joining”
England/Wales IMD 2015 joined to LSOA Shapefiles
<p>This data set includes the Index of Multiple Deprivation (IMD) 2015 data tables, commissioned by the UK Government Department for Communities and Local Government (proper credits are included in the attached technical report). It is worth noting to anyone planning to use this data for strategic planning etc., that the data here is meant to provide an update to IMD 2010, but that most of the underlying data was generated in 2012-2013. You can read more about the underlying technical details in the included PDF files, and find information about specific data columns there as well.</p> <p>IMD data is geo-referenced to Lower Layer Super Output Areas (LSOA) areas, but this data is not provided as a shapefile for easy download, so for convenience sake, I have joined IMD 2015 with the Census Output Area (LSOA) shapefiles which are produced separately by the Office for National Statistics UK (ONS). Both of these data sets are produced under an Open Government License, so I am redistributing these files on that basis. The primary purpose of placing these datasets in a repository is to enable their usage in reproducible research outputs.</p> <p>LSOA shapefiles are produced in several versions. This is because the canonical LSOA polygon data is produced at a high resolution, so the actual shapefiles can be quite large (up to 1GB). ONS produces a series of "generalised" shapefiles which have simplified geometries, and are thus smaller in size. Options for shapefiles include the following:</p> <p>- Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands);<br> - Full resolution - clipped to the coastline (Mean High Water mark);<br> - Generalised (20m) - clipped to the coastline (Mean High Water mark);<br> - Super generalised (200m) - clipped to the coastline (Mean High Water mark) and<br> - Ultra generalised (500m) - clipped to the coastline (Mean High Water mark).</p>
Figure 2. A neighbour-joining tree using 604 cytochrome c oxidase subunit I in Phylogenetic relationship among slender loris species (Primates, Lorisidae: Loris) in Sri Lanka based on mtDNA CO1 barcoding
Figure 2. A neighbour-joining tree using 604 cytochrome c oxidase subunit I (CO1) sequences from 7 different slender loris (Loris) taxas found in Sri Lanka with their external appearance.
Figure 3. A neighbor joining tree using cytochrome c oxidase subunit 1 in DNA barcoding of black cherry aphid Myzus cerasi (Fabricus, 1775) (Hemiptera: Aphididae) populations collected from Prunus avium and Prunus cerasus
Figure 3. A neighbor joining tree using cytochrome c oxidase subunit 1 sequences from Myzus cerasi populations.
Fig. 1. A Neighbor Joining tree phylogram comparing 316 in Zoonotic and vector-borne pathogens in tigers from a wildlife safari park, Italy
Fig. 1. A Neighbor Joining tree phylogram comparing 316 bp 18S rRNA DNA Hepatozoon canis sequences from tigers, herein in bold, to other Hepatozoon spp. GenBank deposited sequences with Babesia canis as outgroup. Sequences are presented by GenBank accession number, host species and country of origin.
Effectiveness of Joins® for Managing Lumbar Facetogenic Pain
ClinicalTrials.gov study NCT06204952. IPD Sharing: NO. Countries: 0. Publications: 10.
Data from: Joined at the hip: linked characters and the problem of missing data in studies of disparity
Open the record for dataset details and reuse information.
ARID1A recruits non-homologous end joining factors to DNA breaks induced by G4 ligands
GEO Series GSE295547. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Switch tandem repeats influence the choice of the alternative end-joining pathway in class switch recombination
GEO Series GSE174296. Mus musculus. 26 samples. Type: Other.
Histone variant macroH2A1.1 enhances non-homologous end joining-dependent DNA double-strand-break repair and reprogramming efficiency of human iPSC
GEO Series GSE164396. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
The PAXX and XLF DNA Repair Factors are Functionally Redundant in Joining DNA breaks in a G1-arrested Progenitor B Cell Line
GEO Series GSE84102. Mus musculus. 27 samples. Type: Other.
ATM and 53BP1 regulate alternative end joining-mediated V(D)J recombination
GEO Series GSE246239. Mus musculus. 42 samples. Type: Other.
Homeodomain-containing protein PRRX1 senses DNA double-strand breaks and anchors the Ku heterodimers to promote non-homologous end-joining
GEO Series GSE265857. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Orientation-Specific Joining of AID-initiated DNA Breaks Promotes Antibody Class Switching
GEO Series GSE71005. Mus musculus. 122 samples. Type: Other.
Transcriptome changes of Hela cells with blocking classical and alternative non-homologous end-joining (NHEJ) pathways
GEO Series GSE135274. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Polymerase theta-mediated end-joining repairs persistent G1-induced DNA breaks in S/G2.
GEO Series GSE306291. Mus musculus. 25 samples. Type: Other.
Cooperativity between classical DNA end-joining and p53 in carboplatin resistance in human ovarian cancer cells
GEO Series GSE173579. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Long-range joining of intra-chromosomal DNA double-strand breaks
GEO Series GSE53755. Mus musculus. 13 samples. Type: Genome variation profiling by high throughput sequencing.
Ku70 and Ligase IV deficiencies reveal distinct alternative end-joining outcomes in G1-arrested progenitor B cells
GEO Series GSE162453. Mus musculus. 116 samples. Type: Other.
Full Data Tables: MeGNN-Join: Predicting Table Joinability in Data Lakes using a Metadata Knowledge Graph
<p>This is the anonymous upload for the full tabular datasets associated with the paper submission "MeGNN-Join: Predicting Table Joinability in Data Lakes using a Metadata Knowledge Graph".</p> <p>The full data is not strictly necessary for reproduction for our results - however, they would be necessary if one was interested in creating a whole new set of table joinability ground-truth data to evaluate our methods over. </p> <p>Please note that the full data is fairly large when unzipped (~30-40gb).</p>
A subsection of England and Wales EPC households, joined with PPD data, used for simulation modelling
<p>If you want to give feedback on this dataset, or wish to request it in another form (e.g csv), please fill out this survey <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.7322967">here</a>. We are a not-for-profit research organisation keen to see how others use our open models and tools, so all feedback is appreciated! It's a short form that takes 5 minutes to complete. </p> <p><strong>Important Note: Before downloading this dataset, please read the License and Software Attribution section at the bottom.</strong></p> <p>This dataset aligns with the work published in Centre for Net Zero's report "Hitting the Target". In this work, we simulate a range of interventions to model the situations in which we believe the UK will meet its 600,000 heat pump installation per year target by 2028. For full modelling assumptions and findings, read our <a href="https://www.centrefornetzero.org/res/hitting-the-target/">report on our website</a>.</p> <p>The code for running our simulation is open source <a href="https://github.com/centrefornetzero/domestic-heating-abm">here</a>.</p> <p>This dataset contains over 9 million households that have been address matched between Energy Performance Certificates (EPC) data and Price Paid Data (PPD). The code for our address matching is <a href="https://github.com/centrefornetzero/epc-ppd-address-matching">here</a>. Since these datasets are Open Government License (OGL), this dataset is too. We basically model specific columns from various datasets, as set out in our methodology section in our report, to simplify and clean up this dataset for academic use. License information is also available in the appendix of our report above.</p> <p>The EPC data loaders can be found <a href="https://github.com/centrefornetzero/epc-england-wales-parquet">here</a> (the data is <a href="https://epc.opendatacommunities.org/">here</a>) and the rest of the schemas and data download locations can be found <a href="https://github.com/centrefornetzero/bigquery-schemas">here</a>.</p> <p>Note that this dataset is not regularly maintained or updated. It is correct as of January 2022. The data was curated and tested using dbt via <a href="https://github.com/centrefornetzero/domestic-heating-data">this Github repository</a> and would be simple to rerun on the latest data.</p> <p>The schema / data dictionary for this data can be found <a href="https://github.com/centrefornetzero/domestic-heating-data/blob/main/cnz/models/marts/domestic_heating/domestic_heating.yml#L5">here</a>.</p> <p>Our recommended way of loading this data is in Python. After downloading all "parts" of the dataset to a folder. You can run:</p> <p>```</p> <p>import pandas as pd</p> <p>data = pd.read_parquet("path/to/data/folder/")</p> <p>```</p> <p> </p> <p><strong>Licenses and software attribution</strong>:</p> <p><em>For EPC, PPD and UK House Price Index data</em>:</p> <p>For the EPC data, we are permitted to republish this providing we mention that all researchers who download this dataset follow <a href="https://epc.opendatacommunities.org/docs/copyright">these copyright restrictions</a>. We <strong>do not explicitly release any Royal Mail address data</strong>, instead we use these fields to generate a pseudonymised "address_cluster_id" which reflects a unique combination of the address lines and postcodes, as well as other metadata. When viewing <a href="https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/what-is-personal-data/what-is-personal-data/">ICO and GDPR guidelines</a>, this still counts as personal data, but we have gone to measures to pseudonymise as much as possible to fulfil our obligations as a data processor. You <strong>must read this carefully before downloading the data</strong>, and ensure that you are using it for the research purposes as determined by this copyright notice.</p> <p>Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.</p> <p>Contains OS data © Crown copyright and database right 2022.</p> <p>Contains Office for National Statistics data licensed under the Open Government Licence v.3.0.</p> <p>The OGL v3.0 license states that we are free to:</p> <ul> <li>copy, publish, distribute and transmit the Information;</li> <li>adapt the Information;</li> <li>exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.</li> </ul> <p>However we must (where we do any of the above):</p> <ul> <li>acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence;</li> </ul> <p>You can see more information <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">here</a>.</p> <p><em>For XOServe Off Gas Postcodes</em>:</p> <p>This dataset has been released openly for all uses <a href="https://www.cse.org.uk/projects/view/1259#GB_postcodes_off_the_mains_gas_grid">here</a>.</p> <p><em>For the address matching:</em></p> <p>GNU Parallel: O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014</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.