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.
2,009
datasets available to search
ShareScore release 0.9.0
Dataset results
2,009 results for “Disruption”
TET2 and TET3 loss disrupts small intestine differentiation and homeostasis
GEO Series GSE200230. Mus musculus. 18 samples. Type: Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Loss of Ring1B catalytic activity causes a pronounced reduction in H3K27me3 deposition yet minimally disrupts the expression of target genes
GEO Series GSE69978. Mus musculus. 21 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing.
Disease-related AUTS2 disruption causes an imbalance of neuronal/glial lineage specification through the WNT/β-catenin pathway (RNA-Seq).
GEO Series GSE255183. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Potential toxicity of inorganic ions in the particulate matter: ion permeation in lung and disruption of cell metabolism
GEO Series GSE182629. Homo sapiens; Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Disruption of DNA methylation underpins the neuroinflammation induced by targeted CNS radiotherapy
GEO Series GSE289218. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Disrupted functional neworks in autism underlie early brain maldevelopment and provide accurate classification
GEO Series GSE42133. Homo sapiens. 147 samples. Type: Expression profiling by array.
LATS kinase-mediated selective disruption of CTCF genomic binding
GEO Series GSE114319. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
BET inhibition disrupts transcription but retains enhancer-promoter contact [Capture-C]
GEO Series GSE139434. Homo sapiens. 30 samples. Type: Other.
Topological demarcation by HMGB2 is disrupted early upon senescence entry and induces CTCF clustering across cell types [DROP-seq]
GEO Series GSE102090. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Heterogeneous Cardiac- and Neural Crest-Derived Aortic Smooth Muscle Cells have Similar Transcriptional Changes after TGFβ Signaling Disruption [scRNA-seq-of-human-aortic-media-vascular-cells]
GEO Series GSE253934. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
SARS-CoV-2 ORF3a expression in brain disrupts the autophagy-lysosomal pathway, impairs sphingolipid homeostasis and drives neuropathogenesis
GEO Series GSE224290. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Combined epigenetic therapy with FACT and BET inhibitors remodels chromatin and disrupts oncogenic transcription in Diffuse Midline Glioma [Nascent RNA-Seq]
GEO Series GSE299509. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Global early replication disrupts gene expression and chromatin conformation in a single cell cycle: replication timing
GEO Series GSE199447. Saccharomyces cerevisiae. 24 samples. Type: Other.
Is disruption of sleep quality a consequence of severe Covid-19 infection? A case-series examination
<p>The Covid-19 outbreak put enormous stress on the health system worldwide, and objective data to handle the emergency are still needed. We aimed to objectively assess the consequence of severe symptoms of Covid-19 infection on sleep quality through wrist actigraphy monitoring of four patients during the sub-acute recovery stage of the disease. The sleep of those patients who had experienced the most severe respiratory symptoms and who had needed prolonged intensive care unit (ICU) stay showed lower Sleep Efficiency and Immobility Time and higher Fragmentation Index compared to those patients who had experienced only mild respiratory symptoms and not requiring ICU stay. Wrist actigraphy assessment provided important clinical information about the sleep and activity levels of Covid-19 patients during the post-acute rehabilitation management.</p>
Genetic characterisation of two novel heterozygous variants disrupting H bonds and 53 GPI gene variants
<p>55 Missense/nonsense mutations in the <i>GPI</i> gene.</p>
Experimental habitat fragmentation disrupts host-parasite interaction over decades via life-cycle bottlenecks
Habitat loss and fragmentation are likely to seriously impact parasites, a less studied but critical component of ecosystems, yet we lack long-term experimental evidence. Parasites structure communities, increase connectivity in food webs, and account for a large proportion of an ecosystem's total biomass. Food web models predict that parasites with multiple obligate hosts are at greater risk of extinction because the local extinction, or reduction in abundance, of any host will result in a life-cycle bottleneck for the parasite. We examine the response of a parasite and its multiple hosts to forest fragmentation over 26 years in the Wog Wog Habitat Fragmentation Experiment in southeastern Australia. The parasite is the nematode Hedruris wogwogensis, its intermediate host is the amphipod, Arcitalitrus sylvaticus, and its definitive host is the skink, Lampropholis guichenoti. In the first decade after fragmentation, nematodes completely disappeared from the matrix (plantation forestry) and all but disappeared from their definitive host (skinks) in fragments, and by the third decade after fragmentation had not appreciably recovered anywhere in the fragmented landscape compared to continuous forest. The low prevalence of the nematode in the fragmented landscape was associated with the low abundance of one or the other host in different decades: low abundance of the intermediate host (amphipod) in the first decade and low abundance of the definitive host (skink) in the third decade. In turn, the low abundance of each host was associated with changes to the abiotic environment over time due largely to the dynamically changing matrix as the plantation trees grew. Our study provides rare long-term experimental evidence of how disturbance can cause local extinction in parasites with life cycles dependent on more than one host species through population bottlenecks at any life stage. Mismatches in the abundance of multiple hosts over time are likely to be common following disturbance, thus causing parasites with complex life cycles to be particularly susceptible to habitat fragmentation and other disturbances. The integrity of food webs, communities, and ecosystems in fragmented landscapes may be more compromised than presently appreciated due to the sensitivity of parasites to habitat fragmentation.
Data for 'Partial Tidal Disruption Events: The Elixir of Life'
<p>This folder includes all the data for the paper titled: Partial Tidal Disruption Events: The Elixir of Life</p> <p>Simulations were run with publicly available phantom code (https://github.com/danieljprice/phantom; https://phantomsph.readthedocs.io/en/latest/) and Kepler code (https://2sn.org/kepler/doc/Setup.html)</p> <p>We used splash (https://github.com/danieljprice/splash.git) for making Figures 2, 7, 8, 9, 10, 12, 14, B2, F1.</p> <p>We have three folders called s1_kepler, s3_kepler and s10_kepler. They contain the KEPLER models .wnd and .cnv files along with the dumps for ZAMS, MAMS and TAMS</p> <p>We have saved the PHANTOM relaxed models in star_(mass_of_star)_(evolution_stage) along with mapping back evolution in KEPLER files (link.dat, .wnd, .cnv). We have also saved Makefile, star.in, star.setup, tde.comp files, linkg.</p> <p>We have saved the PHANTOM simulation data in folders named star(mass_of_star)_(evolution_stage)_(beta_val). These include the .in, .tdeparams, ptok file, tde dump file used for analysis, KEPLER mapping back files (link.dat, .wnd, .cnv), .ev files. </p> <p>We have saved the KEPLER models that had the same mass as the remnants in star(mass_of_star)_(evolution_stage)_(beta_val)_kepler folders. We provide the generator file, .wnd, .cnv files. </p> <p>We have saved the KEPLER models that were generated by replacing the composition in the ptok files with the original star composition in the star_(mass_of_star)_(evolution_stage)_(beta_val)_strip</p> <p>We have saved the KEPLER models that were run until the He4 in the central bin was same as remnant He4 in centre, in the star(mass_of_star)_(evolution_stage)_(beta_val)_he4_same</p> <p>We have saved the PHANTOM simulation data for zero energy orbits run in star(mass_of_star)_(evolution_stage)_(beta_val)_zero folders. </p> <p>We have saved the geodesic files in the geodesic folder. It includes the .py files used to run jobs in parallel, and interpolation files. We have also saved the rp values used in the simulations run. </p> <p>We have saved the scripts used to run the KEPLER models (mapping back and stripping) in the python_code folder. We have also saved an EXCEL file which includes all the PHANTOM simulations data, along with the correct beta values shown in the paper. </p> <p><br>-----------------------------------------------------------------------------------------------------</p> <p>For the figures from the publication, </p> <p>Figure 1 uses the data from s1_kepler, s3_kepler and s10_kepler folders. </p> <p>Figure 2 uses data files present in the star(mass_of_star)_(evolution_stage)_(beta_val) folders. The folders for 3 solar MID have sub-folders starting with fig2 which contain the PHANTOM dump files. </p> <p>Figure 3 uses the data from solar3_mid_beta(value) folders. We have included the .ev files which were used to make this plot. </p> <p>Figure 4 uses the mass of the remnant as function of time. We have included the dump_info files that can be used to make this plot. </p> <p>Figure 5 and 6 use the mass of the remnant. You can access this information from Table 1 and 2 of the paper. </p> <p>Figure 7 uses the remnant files from the star1, star3 and star10 MID models. </p> <p><br>Figure 8 uses the ptok files and tde dump files. These are all included in the folders. </p> <p>Figure 9 uses dump files which are included in a sub-folder in the star3_mid_beta18 folder. </p> <p>Figure 10 requires the dump files along with files including the binned rotation profile and the break-up velocities. All of this is present in the folders where .exact1 and .exact2 are the binned and break-up profiles.</p> <p>Figure 11 can be generated by using the ptok files as they include binned rotation profile and radius.</p> <p>Figure 12 uses data files from the star3_mid_beta18 folder. We have included the remnant files. The same folder includes vphi files for Figure 13. </p> <p>Figure 14 requires the dump files along with tde.comp files. All tde.comp files are present in star_(mass_of_star)_(evolution_stage) folders. Dumps are in star_(mass_of_star)_(evolution_stage)_beta(value)folders. </p> <p>Figure 15 uses the files provided in the folders ending with _he4_same. </p> <p>Figure 16 and 17 can be generated by using the .wnd files provided in the star_(mass_of_star)_(evolution_stage) and star_(mass_of_star)_(evolution_stage)_beta(value) folders. The same files can be used for Figure 18 and Figure 19. </p> <p>Figure 20 can be generated by using the files present in folders ending with _kepler. </p> <p>Figure 21 uses files from folders ending with _strip. </p> <p>Figure 22 uses ptok files which have been provided for all star10_tams_beta(value) folders</p> <p>Figures A1, A2 can be generated using the ptok files and the original model files. </p> <p>Figure B2 uses files from sub-folder called cut_plot in the star3_mid_beta15 folder.</p> <p>Figure E1 uses .wnd files present in star(mass_of_star)_(evolution_stage) folders. </p> <p>Figure E2 uses .wnd files from the star3_tams_beta25 (sub-folder, time_run).</p> <p>Figure F1 uses files from zero and hyperbolic folders. </p> <p><br>-----------------------------------------------------------------------------------------------------</p> <p>To load the .wnd and .cnv, use windata and conv modules from python/source code written by Alexander Heger (KEPLER).<br>To load files starting with tde_0* use splash or sarracen (https://sarracen.readthedocs.io/en/latest/quick-start.html). <br>To load files starting with ptok (ASCII files), you can use splash or python. <br>To load .ev (ASCII files), you can use splash or python. <br>To load rem_* files or exact* files (ASCII) splash or python can be used. <br>To load link.dat file (ASCII) one can use splash or python. <br>To load KEPLER files containing @ one can use python. These are ASCII files. To convert any KEPLER files containing #, one would need to use Le-data filename to get it into ASCII form. </p> <p>-----------------------------------------------------------------------------------------------------<br>orbit.tdeparams files contain the initial position and velocities required for zero energy orbit. <br>tde.tdeparams files contain the beta, pericentre and starting distance along with mass of SMBH (in code units). </p> <p>star_00000.tmp files are the relaxed stellar profiles that were disrupted by the SMBH. <br>dump_info file is an ASCII file with information about the dumps such as the mass of the remnant, escape velocity, and maximum radius. <br> </p> <p> </p>
Disruption Index Dataset
<p>This dataset was prepared for the Pissarides Review into the Future of Work and Wellbeing, led by Professor Sir Christopher Pissarides (Institute for the Future of Work and London School of Economics).</p> <p>The dashboard for the Disruption Index, which uses this data, can be found <a href="https://di-dash-new-f6a2d1835dc0.herokuapp.com/">here</a>.</p> <p>The Disruption Index Interactive Report - published by IFOW for the Pissarides Review - can be found <a href="https://www.ifow.org/publications/disruption-index-interactive-report">here</a>.</p> <p>We gratefully acknowledge financial support from the Nuffield Foundation. The Pissarides Review into the Future of Work and Wellbeing is a collaboration between the Institute for the Future of Work (IFOW), Imperial College London (ICL) and Warwick Business School.</p>
Artificial intelligence reveals potential Arctic whale aggregation disruption due to climate change
<p>Climate change has been shown to alter the spatial distribution of whales and other marine mammals. Fast changing ocean temperatures may also affect the spatial distribution of whales at a finer scale, namely within populations, including aggregation behaviour. Here, we analyze the impact of climate change on whale aggregation behavior.</p>
data: Early visual deprivation disrupts the mental representation of numbers in visually impaired children.
<p>The datasets contain the raw data of PAE and PE variables.</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.