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.
73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “Decision processes,”
In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions [microarray_dataset1]
GEO Series GSE148681. Mus musculus. 28 samples. Type: Expression profiling by array.
In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions
GEO Series GSE160341. Mus musculus. 40 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing.
In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions [scRNA-seq]
GEO Series GSE160305. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of pre-mRNA 3' end processing reveals a decisive role of human cleavage factor I in the regulation of 3' UTR length: CLIP
GEO Series GSE37398. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
NOD genetic variation influences ab/gd lineage decisions when TCRa is prematurely expressed, but not the process of negative selection.
GEO Series GSE34936. Mus musculus. 59 samples. Type: Expression profiling by array.
In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions [Pofut1_microarray]
GEO Series GSE160225. Mus musculus. 16 samples. Type: Expression profiling by array.
Verification of XAPPORT: a Decision Support App for Physicians Used for Patients Anticoagulated With Rivaroxaban in Terms of Anticoagulation Management in Elective Surgery: Verification Process of Med
ClinicalTrials.gov study NCT02900404. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Subthalamic Deep Brain Stimulation Effects on Decision-making Processing in Patients With Parkinson's Disease
ClinicalTrials.gov study NCT02231645. IPD Sharing: Not stated. Countries: 0. Publications: 0.
In vivo CRISPR screening reveals nutrient signaling processes underpinning CD8+ T cell fate decisions [NICD_microarray]
GEO Series GSE160218. Mus musculus. 8 samples. Type: Expression profiling by array.
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact
<div> <h1>Artefact for CAV 2024</h1> <a href="https://github.com/cxlvinchau/cav2024-experiments#artefact-for-cav-2024"></a></div> <p>This artefact accompanies the submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes".</p> <div> <h2>Contents of this artefact</h2> <a href="https://github.com/cxlvinchau/cav2024-experiments#contents-of-this-artefact"></a></div> <p>This artefact consists of two different folders, namely <code>data</code> and <code>software</code>.</p> <div> <h3>The <code>data</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-data-folder"></a></div> <p>The <code>data</code> folder contains data and results presented in the experimental section, along with models and properties that have been used. For every model and type of query (mean-payoff or reachability), there is a separate folder, containing the following types of files:</p> <ul> <li><code>data.csv</code> - A csv file where each row corresponds to a query and a model the query was considered for. Every row then contains information on the runtimes and model sizes. A detailed explanation of the columns can be found in the <code>README.md</code> of the data folder.</li> <li>PRISM model files with <code>.nm</code> or <code>.prism</code> extension, corresponding to the models used for the experiments.</li> <li>PRISM property files with <code>.props</code> extension, corresponding to the queries used for the experiments.</li> <li>A <code>README.md</code> with notes on the origins of the models.</li> </ul> <div> <h3>The <code>software</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-software-folder"></a></div> <p>The <code>software</code> directory contains our implementation of the presented techniques, including computation of the certificates (aka certification), computation of witnessing schedulers and the computation of minimal witnessing subsystems. It consists of the following components:</p> <ul> <li><code>cpmc</code> contains the Python implementation of the techniques and consists of several submodules: <ul> <li><code>cpmc/core</code> implements classes for representing MDPs and other modeling components</li> <li><code>cpmc/mean_payoff</code> implements the techniques for working with multi-objective mean-payoff queries</li> <li><code>cpmc/reachability</code> implements the techniques for working with multi-objective reachability queries</li> <li><code>cpmc/prism</code> implements the translation of subsystems to PRISM code</li> <li><code>cpmc/test</code> contains unit tests that can be run by navigating into the folder and running <code>pytest .</code></li> </ul> </li> <li><code>experiments</code> contains scripts and utility files for running the experiments: <ul> <li><code>experiments/phil.py</code> Script for running the dining philosophers experiment</li> <li><code>experiments/csn.py</code> Script for running the csn mean-payoff experiment</li> <li><code>experiments/csn_reachability.py</code> Script for running the csn reachability experiment</li> <li><code>experiments/sensors.py</code> Script for running the sensors experiment</li> <li><code>experiments/consensus.py</code> Script for running the consensus (coin) experiment</li> <li><code>experiments/firewire.py</code> Script for running the firewire experiment</li> <li><code>experiments/main.py</code> Command line interface for runnign the experiments</li> </ul> </li> </ul>
Revealing Gender Biases in (TJSP) Court Decisions with Natural Language Processing
<p>Data derived from the realm of the social sciences is often produced in digital text form, which motivates its use as a source for natural language processing methods. Researchers and practitioners have developed and relied on artificial intelligence techniques to collect, process, and analyze documents in the legal field, especially for tasks such as text summarization and classification. In this scenario, we identify an underexplored potential of natural language processing used to delve into human rights issues in the context of artificial intelligence for social good. Qualitative and quantitative social science methods have been used to study matters such as institutional gender biasing in legal settings; however, natural language processing-based approaches can help analyze the issue on a larger scale. The work Revealing Gender Biases in Court Decisions with Natural Language Processing presents a protocol to address the automatic detection of institutional gender biasing in Brazilian courts, which comprises: (a) a pipeline of collection, annotation, and preparation of text extracted from court decisions issued by the São Paulo state Court of Justice in cases of domestic violence and parental alienation, which resulted in two datasets; (b) an experimental protocol of supervised binary classification over the decisions, performed with BERTimbau-based models; (c) methods for evaluating and validating such protocol.</p> <p>Here, we present the two datasets associated with this work: Dataset 1, made of 1,604 decisions issued by the Court between 2012 and 2019 in domestic violence-related criminal cases (DVC), and Dataset 2, made of 49 decisions issued by the Court in the same timeframe in civil and criminal parental alienation-related cases (PAC). Details on the content of each dataset, as well as their pipelines of extraction, annotation, preparation, and use, can be found in the original work, published as a Master's dissertation.</p> <p>The structure of the datasets is presented as follows:</p> <p>├── Dataset 1 (domestic violence cases, DVC): lesao.zip<br>│ ├── files<br>│ └── content<br>├── Dataset 2 (parental alienation cases, PAC): ap.zip<br>│ ├── files<br>│ └── content</p> <ul> <li><strong>files</strong> folder: contains input and output files associated with the pipelines of data extraction, annotation, and preparation as documented in the original work;</li> <li><strong>content</strong> folder: contains TXT and PDF files for each decision.</li> </ul> <p>Please note that, to access and use the datasets, one must abide to a deed of undertaking, whose violation entails legal liability of the breacher. Details on guidelines of legal and ethical compliance regarding this data can be found in the associated publications.</p>
ESI_Electrifying Freight-Modeling the Decision Making Process for Battery Electric Truck Procurement
Open the record for dataset details and reuse information.
ESI_Electrifying Freight-Modeling the Decision Making Process for BET Procurement
Open the record for dataset details and reuse information.
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.