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2,015
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Dataset results
2,015 results for “context”
Transcriptome of human myometrial stem cells in the context of uterine fibroids
GEO Series GSE158515. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
NMT inhibition in the context of MYC levels
GEO Series GSE154336. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
In-Depth Functional Analysis of BRD9 in Fetal Hematopoiesis Reveals Context-Dependent Roles [scRNA-seq]
GEO Series GSE269137. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Mbnl2 loss alters novel context processing and impairs object recognition memory
GEO Series GSE226191. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of murine primary pancreatic acinar cells in the context of Arid1a deficiency
GEO Series GSE166333. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Genomic context mediates position-dependent enhancer function at the Sonic hedgehog locus
GEO Series GSE223757. Mus musculus. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Third-party reanalysis.
RNAseq of two mouse lung cancer cell lines in a DSTYK-altered context
GEO Series GSE212858. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
microRNA Induction of Fatal Differentiation Programs is a Context-selective Vulnerability in Ovarian Cancer Cells [PEO1]
GEO Series GSE67327. Homo sapiens. 12 samples. Type: Expression profiling by array.
ASCL1 Drives Tolerance to Osimertinib in EGFR Mutant Lung Cancer in Permissive Cellular Contexts
GEO Series GSE243569. Homo sapiens. 64 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Comparison of transcriptional profiles from 2W1S-specific CD4+ T cells during 2W1S-expressing Salmonella and Strongyloides ratti infections reveals that infectious context drives effector CD4+ T cell
GEO Series GSE134278. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Single-cell multiomics of neuronal activation reveals context dependent genetic controls of brain disorders
GEO Series GSE312445. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
ASCL1 Drives Tolerance to Osimertinib in EGFR Mutant Lung Cancer in Permissive Cellular Contexts [PDX RNA-seq]
GEO Series GSE243564. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Promoter context-specific positive and negative transcriptional regulation of Glucocorticoid Receptor target genes by coregulator GLP.
GEO Series GSE94646. Homo sapiens. 8 samples. Type: Expression profiling by array.
Context-specific regulation of BMAL1 target genes during the circadian cycle in mammals
GEO Series GSE61775. Mus musculus. 58 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Third-party reanalysis; Expression profiling by high throughput sequencing.
Comparative mRNA Profile Analysis from NAc of Adolescent Male Mice after Binge-Like Alcohol Exposure Eliciting Deficits in Context Fear Extinction Learning.
GEO Series GSE290254. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A proposal From Data Requirements to Semantic Data in the Context of MDA
<p>This work proposes a process for the identification of the requirements associated with the data, along with and a set of transformations in Model Driven Architecture (MDA) context, in order to obtain a semantically annotated dataset, as a result of the unification and alignment of the data in the context of its initial domain. Our proposal identifies four phases (from CIM to code), in which is describe the artifacts and transformations required to progress to the next phase: a target domain model is first obtained from the data requirements, after which the ontological schema and the ontology is generated from the previous model. A domain-specific language (DSL), also proposed in this work, is then used to obtain the semantic data model (the DSL code), which gener-ates the final semantic dataset. We have validated the proposal by studying two cases: one with data from the public transport domain and the other with data concerning those affected by the COVID-19 pandemic.</p>
HRM in disruptive contexts
<p>PLS SEM methodology used to analyse quantiatative data from physicians.</p>
CENTRINNO_Pilots' Context Analysis (Doughnut Economy Graphics)
<p>These graphics visualize collected data on urban ecosystems for nine CENTRINNO pilots. Based on the Doughnut Economy model developed by Kate Raeworth, we adapted the approach to map the baseline/ context of urban ecosystems across eight pillars. </p>
MIMOSA: dataset behind the interactive tool for selection of nut harvesting machinery in agroforestry context (MachInes en Methodes voor de OogSt van noten in Agroforestry)
<p>This dataset contains all the information which is used to run the calculations in the interactive decision support tool "MIMOSA" (MachInes en Methodes voor de OogSt van noten in Agroforestry), intended to select nut harvesting machinery in an agroforestry context. This tool is developed for use in Belgium and The Netherlands, and is currently only available in Dutch.</p> <p>In recent years, a growing interest in agroforestry systems combined with nut production has been observed in Belgium and the Netherlands. Thereby, it is important to also pay sufficient attention to the harvesting method, even before the trees are planted. For example, grouped planting of varieties according to harvest time is recommended, or the distance between tree rows also determines the possibility of being able to access the field with certain machines. The use of harvesting machines usually also requires an almost perfectly even surface. But the harvesting method itself can also save a lot of time and money.</p> <p>When harvesting, one can wait until the green husk bursts and the nut falls down naturally or use shaking machines to make the nuts fall faster and at the same time. A choice also has to be made on whether to use nets under the trees or not. If nets are not used, consideration should be given to what other method of harvesting can be used: manually for smaller plots or with different types of pick-up machines or a suction system for larger plantings.</p> <p>The objective of MIMOSA is to support making the right choices considering scale and farm specific preferences, labour availability, etc.</p> <p><span>Based on your personal data, this interactive calculation tool gives you targeted advice for cost-efficient harvesting of your acreage of nuts. Together with the fixed parameters (working width, harvesting speed, fuel consumption, purchase price, depreciation period and machine loading capacity), it then calculates the cost price of harvesting your acreage with different machines for your specific situation (both with and without shaker). Of course, we do want to stress that this is a proposal based on theoretical calculations and only a limited number of machines are included in this calculation tool. It therefore remains important to check for yourself whether the proposed machines are suitable for your farming system.</span></p> <p>The use of the MIMOSA tool is free, and it can be accessed through this link <a href="https://bdbnet.bdb.be/pls/apex/f?p=147:10">https://bdbnet.bdb.be/pls/apex/f?p=147:10</a></p> <p>The background info sheet can be read here: <a href="https://www.agroforestryvlaanderen.be/nl/nieuws/oogst-van-noten-welke-methodes-en-machines">https://www.agroforestryvlaanderen.be/nl/nieuws/oogst-van-noten-welke-methodes-en-machines</a> (in Dutch).</p> <p>More info? Contact us through info@agroforestryvlaanderen.be </p>
Evaluation Data for "Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation"
<p><strong>Contents</strong><br> <br> The four CSV files are the data used for the evaluation in:</p> <ul> <li>Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.</li> <li>DOI: <a href="http://doi.org/10.1007/978-3-030-45439-5_15">10.1007/978-3-030-45439-5_15</a></li> <li>Code: <a href="https://github.com/IllDepence/ecir2020">github.com/IllDepence/ecir2020</a></li> </ul> <p>The evaluation was conducted in a citation re-prediction setting.</p> <p><strong>CSV Format</strong></p> <ul> <li>7 columns divided by \u241E <ol> <li>cited document ID <ul> <li>for *_nomarker.csv: citation marker position ambiguous</li> <li> for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context</li> </ul> </li> <li>adjacent cited document IDs <ul> <li>only given in citrec_unarxive_*.csv</li> <li>divided by \u241F</li> <li>order matches 'CIT' markers in citation context</li> </ul> </li> <li>citing document ID</li> <li>citation context</li> <li>MAG field of study IDs <ul> <li>divided by \u241F</li> </ul> </li> <li>predicate:argument tuples generated based on PredPatt <ul> <li>JSON</li> </ul> </li> <li>noun phrases <ul> <li>for *_nomarker.csv: divided by \u241F</li> <li>for *_withmarker.csv: <ul> <li>divided by \u241D into</li> <li>noun phrases</li> <li>noun phrase directly preceding citation marker</li> </ul> </li> </ul> </li> </ol> </li> </ul> <p><strong>Data Sources</strong></p> <ol> <li>citrec_unarxive_cs_withmarker.csv <ul> <li>data set <ul> <li>unarXive</li> <li>Paper DOI: <a href="http://doi.org/10.1007/s11192-020-03382-z">10.1007/s11192-020-03382-z</a></li> <li>Data DOI: <a href="http://doi.org/10.5281/zenodo.2553522">10.5281/zenodo.2553522</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science</li> <li>cited doc is cited at least 5 times</li> </ul> </li> </ul> </li> <li>citrec_mag_cs_en.csv <ul> <li>data set <ul> <li>Microsoft Academic Graph (MAG)</li> <li>Paper DOI: <a href="http://doi.org/10.1145/2740908.2742839">10.1145/2740908.2742839</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science and in English</li> <li>citing doc abstract in MAG given</li> <li>cited doc is cited at least 50 times</li> </ul> </li> </ul> </li> <li>citrec_refseer.csv <ul> <li>data set <ul> <li>RefSeer</li> <li>Paper URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">ojs.aaai.org/index.php/AAAI/article/view/9528</a></li> <li>Data URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">psu.app.box.com/v/refseer</a></li> </ul> </li> <li>filter <ul> <li>for citing and cited docs title, venue, venuetype, abstract, and year not NULL</li> </ul> </li> </ul> </li> <li>citrec_acl-arc_withmarker.csv <ul> <li>data set <ul> <li>ACL ARC</li> <li>Paper URL: <a href="http://aclanthology.org/L08-1005">aclanthology.org/L08-1005</a></li> <li>Data URL: <a href="http://acl-arc.comp.nus.edu.sg/">acl-arc.comp.nus.edu.sg/</a></li> </ul> </li> <li>filter <ul> <li>cited doc has a DBLP ID</li> </ul> </li> </ul> </li> </ol> <p> </p> <p><strong>Paper Citation</strong></p> <pre>@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, } </pre>
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.