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Dataset results
720 results for “Identification data”
Identification of viruses associated with larvae of the dragonfly Leucorrhinia dubia, and damselfly Coenagrion puella from RNA sequencing data
Open the record for dataset details and reuse information.
Fig. 4. Brachistosternus diaguita n in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 4. Brachistosternus diaguita n. sp. habitus. A, B. Male. A. Dorsal aspect, B. Ventral aspect. C, D. Female, C. Dorsal aspect. D. Ventral aspect. Scale bar: 1 cm.
Identification of differential gene expression in blood vessels from patients with uremia (data set 1)
GEO Series GSE38750. Homo sapiens. 34 samples. Type: Expression profiling by array.
Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data [timeSeries]
GEO Series GSE71721. Homo sapiens. 33 samples. Type: Expression profiling by array.
Tropical CPR identification data for Organization Metric
There are three kinds of files are in one zip file. 1) Cloud-Precipitation (hybrid) regime information projected onto hourly IMERG precipitation data (Version Pr6x7 in 15S-15N, 2000.06.01-2021.06.29 (7699 days), 24 steps per day, -24.5 to 24.5N (nlat=50), and -179.5 to 179.5 (nlon=360); Binary, 2-byte integer) 2) Example python codes to calculate ABCOP and other organization metrics 3) Calculated organization metrics in select domain from CPR data (#1) [Reference: 2022JD036665 (AGU JGR-A submitted)] [See also https://github.com/DJ4seasons/Organization-Metric-ABCOP]
Identification of genes associated with Periodontal pathogens induced endothelial dysfunction using RNA-seq data
GEO Series GSE222136. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
De novo assembly of the desert tree Haloxylon ammodendron (C. A. Mey.) based on RNA-Seq data provides insight into drought response, gene discovery and marker identification
GEO Series GSE63970. Haloxylon ammodendron. 5 samples. Type: Expression profiling by high throughput sequencing.
ZGA-Timer: a framework for the identification of key zygotic genome activation genes from time-series RNA-seq data
GEO Series GSE174530. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
ChIPseq identification of DDIT3 genomic binding sites - correlations with gene expression data reveal dimerization partners and target genes
GEO Series GSE153869. Homo sapiens. 44 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Identification of a robust gene signature that predicts breast cancer outcome in independent data sets [UCSF]
GEO Series GSE123833. Homo sapiens. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Childhood Cancer Data Initiative (CCDI): Identification and Targeting of Treatment Resistant Progenitor Populations in T-cell Acute Lymphoblastic Leukemia
Our goal with this study was to identify the mechanisms of treatment resistance in T-cell acute lymphoblastic leukemia (ALL) using single-cell genomics. We profiled 40 T-ALL cases from the Children's Oncology Group AALL0434 clinical trial using CITE-seq/snATAC-seq, capturing a breadth of immunophenotypes, including early T-cell precursor (ETP) and near-ETP/non-ETP subtypes. By integrating analyses of T-ALL cells with the normal T-cell developmental trajectory, the study identified a specific subgroup of leukemia cells resembling bone marrow progenitors (BMP-like cells). These BMP-like cells were associated with treatment failure and poor overall survival in T-ALL patients. Overall, this study reveals comprehensive multiomic signatures for rapidly assessing risk and tailoring targeted treatment for high-risk T-ALL patients.
Training data for the shared task Ideology and Power Identification in Parliamentary Debates - with errors please do not use!
<p><strong>This dataset is an early release with errors. Please do not use this data set. The official dataset for the shared task will be announced on <a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">the shared task webpage</a>.</strong></p>
Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.
GEO Series GSE276511. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Data-driven identification of small molecules inducing Brown Adipocyte differentiation
GEO Series GSE271635. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Expression data from bovine oocyte; Identification of molecular markers for oocyte competence in bovine cummulus cells
GEO Series GSE65269. Bos taurus. 6 samples. Type: Expression profiling by array.
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p> </p> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p> </p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Basic data for manuscript "Identification Markers, Structural Characteristics and Evolution Mechanism of Strike-Slip Faults in the Southeastern Sichuan Basin, Southwestern China"
<p>The basic data used for the research involved in the manuscript "Identification Markers, Structural Characteristics and Evolution Mechanism of Strike-Slip Faults in the Southeastern Sichuan Basin, Southwestern China" includes the following figures: the position of the key research area in map view (numbered 00). Coherence attribute slices (numbered 01 to 19) of important seismic reflection interfaces for research obtained from 3D seismic data volume of each key research area. Horizontal time slices (numbered 20 to 24) obtained at equal intervals (20ms or 50ms) from 3D seismic data volume in each key research area. Seismic sections obtained from 3D seismic data volume of each key research area and seismic sections perpendicular to strike-slip fault for detailed analysis are also obtained at equal intervals (numbered 25 to 35). Photographs obtained from field observations were also provided (numbered 36 to 39). See the document "Details of basic data" for details of the above pictures.</p>
Data for the manuscript "Identification and structural studies of the crucial RNAi target of SARS-CoV-2 through molecular dynamics simulations"
<p>Simulation datasets for manuscript titled "<strong>Identification and structural studies of the crucial RNAi target of SARS-CoV-2 through molecular dynamics simulations"</strong></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.