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.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “Ites”
Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE
<p>This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset.</p> <p>This dataset has several signatures:</p> <ul> <li>It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</li> <li>The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect).</li> </ul> <p>This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial).</p> <p>The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect.</p> <p>To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine.</p> <p>In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features.</p> <p>Column names:</p> <p> Trial ID: 'trial_id'<br> Experiment group label: 'treatment_group_key'<br> Outcome variable (classification label): 'conversion'<br> Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']<br> True underlying control conversion probability: 'control_conversion_prob'<br> True underlying treatment conversion probability: 'treatment1_conversion_prob'<br> True treatment effect: 'treatment1_true_effect'</p>
Kasus ITE Pengadilan Negeri Surabaya 2021
<p>Dataset of cybercrimes committed by people living in Surabaya, East Java, Indonesia in 2021. This method was conducted by assorting information in regards of the decision made by the Supreme Court in Surabaya district related to cybercrimes violating Information and Electronic Transactions Laws (ITE Laws) regulating activities carried out using computer, computer networks, and/or other electronic media. The information was collected from the official website of the District Court of Surabaya, <a href="http://sipp.pn-surabayakota.go.id/">http://sipp.pn-surabayakota.go.id</a>. </p>
ITE Typing dataset
<p>A large-scale mobile typing dataset contains 46 755 participants typing sentences in English and 8661 participants in Finnish on their own mobile devices. Participants used various iPhone and Android devices with different operation system versions. The data was collected between 2019 and 2020 by the Computational Behaviour Lab of Aalto University. The user's typing operations and use of Intelligent Text Entry (ITE) methods (Autocorrection and Suggestion Bar) are labelled on a keystroke level. The dataset enables analysis of the effects of the user demographics and the usage and accuracy of ITE methods on typing. The dataset also has a separate table for all ITE corrected and predicted words e.g. for the ITE error analysis. </p> <p>Code repository: <a href="https://github.com/aalto-speech/ite-typing-dataset/">https://github.com/aalto-speech/ite-typing-dataset/</a></p> <p> </p> <p>Citation:</p> <p>Leino, Katri, Markku Laine, Mikko Kurimo, and Antti Oulasvirta. Mobile Typing with Intelligent Text Entry: A Large-Scale Dataset and Results. 2024. <a href="https://doi.org/10.21203/rs.3.rs-4654512/v1" rel="nofollow">https://doi.org/10.21203/rs.3.rs-4654512/v1</a></p> <p> </p>
Generation of Tumor Antigen-Specific iPSC-Derived Thymic Emigrants (iTE) using a Novel 3D Thymic Culture System
GEO Series GSE105110. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
A Clinical Investigation of the Benefit: BTE Hearing Aids Versus ITE Hearing Aids
ClinicalTrials.gov study NCT04168229. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of gene expression levels in parental U87 cells and U87 tumor spheres treated with or without ITE
GEO Series GSE67986. Homo sapiens. 3 samples. Type: Expression profiling by array.
Activation of AhR by ITE in DCs induces differential gene expression over time.
GEO Series GSE155712. Homo sapiens; Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
Treatment with nanoliposomes loaded with ITE and MOG(35-55) ameliorates EAE
GEO Series GSE155711. Mus musculus. 60 samples. Type: Expression profiling by high throughput sequencing.
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.