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.
39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “ATL”
Data Set for Predicting the Performance of ATL Model Transformations
<p>Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models that were transformed. We provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Additionally, we calculated the mean absolute percentage error in % and the 95th percentile of the absolute percentage error in % for each experiment and provide these results. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models, the Java projects used to measure the execution time of the transformations, and other supporting scripts, e.g. for the analysis of the results.</p> <p>A short introduction with a quick start guide can be found in README.md and a detailed documentation in documentaion.pdf.</p>
Data Set for Enhanced Performance Prediction of ATL Model Transformations
<p>Model transformation languages are domain-specific languages, which are designed to comfortably define transformations. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. In particular, we focused on how to predict the performance of transformations that also transform attributes whose values can have arbitrary size. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models we used. In this data set, we provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Subcutaneous Recombinant Human IL-15 (s.c. rhIL-15) and Alemtuzumab for People With Refractory or Relapsed Chronic and Acute Adult T-cell Leukemia (ATL)
ClinicalTrials.gov study NCT02689453. IPD Sharing: YES. Countries: 1. Publications: 3.
Efficacy and Safety of Oral HBI-8000 in Patients With Relapsed or Refractory Adult T Cell Lymphoma (ATL)
ClinicalTrials.gov study NCT02955589. IPD Sharing: NO. Countries: 1. Publications: 1.
Phase I Study of KW-0761 in Relapsed Patients With CCR4-Positive ATL and PTCL
ClinicalTrials.gov study NCT00355472. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Treatment of Tac-Expressing Cutaneous T-Cell Lymphoma (CTCL) and Adult T-Cell Leukemia (ATL) With Yttrium-90 Radiolabeled Anti-Tac
ClinicalTrials.gov study NCT00001249. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Efficacy and Safety of ATL-962 in Obese Diabetics
ClinicalTrials.gov study NCT00156897. IPD Sharing: Not stated. Countries: 5. Publications: 1.
SEEG Guided RF-TC v.s. ATL for mTLE With HS
ClinicalTrials.gov study NCT03941613. IPD Sharing: YES. Countries: 1. Publications: 2.
Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models
<p>Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.</p> <p>In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.</p> <p>However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
KW-0761 or Investigator's Choice in Subjects With Previously Treated Adult T-cell Leukemia-Lymphoma (ATL)
ClinicalTrials.gov study NCT01626664. IPD Sharing: Not stated. Countries: 6. Publications: 0.
Influence of ATL-type Helios overexpression or WT-Helios/Ikaros knockdown on gene expression profile in Jurkat cells
GEO Series GSE41796. Homo sapiens. 15 samples. Type: Expression profiling by array.
CTCL and ATL cell lines, mir-150 transfected versus controls
GEO Series GSE49308. Homo sapiens. 8 samples. Type: Expression profiling by array.
miRNA expression profiling in Adult T-cell Leukemia (ATL) cells and in Normal CD4+ T-cells
GEO Series GSE31629. Homo sapiens. 62 samples. Type: Non-coding RNA profiling by array.
Immunomodulatory drugs (IMiDs)- or cereblon E3 ligase modulator (CELMoD)-induced growth suppression of Adult T-Cell Lymphoma/Leukemia (ATL) cells and the intrinsic rolls of cereblon for down-regulatio
GEO Series GSE221056. Homo sapiens. 6 samples. Type: Expression profiling by array.
Whole genome gene expression profiling in CD4+ T-cells, FACS-sorted based on CADM1/CD7 expression levels, in Adult T-cell Leukemia-lymphoma (ATL) patients, HTLV-1 carriers, and in healthy volunteers.
GEO Series GSE55851. Homo sapiens. 21 samples. Type: Expression profiling by array.
Whole genome gene expression profiling in Adult T-cell Leukemia (ATL) cells and in Normal CD4+ T-cells
GEO Series GSE33615. Homo sapiens. 73 samples. Type: Expression profiling by array.
The endogenous HBZ interactome in ATL leukemic cells reveals an unprecedented complexity of host interacting partners involved in RNA splicing
GEO Series GSE206085. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Efficacy and Safety of Activated T Lymphocytes (ATL) in Hepatocellular Carcinoma
ClinicalTrials.gov study NCT05304481. IPD Sharing: NO. Countries: 1. Publications: 0.
Pembrolizumab and a Vaccine (ATL-DC) for the Treatment of Surgically Accessible Recurrent Glioblastoma
ClinicalTrials.gov study NCT04201873. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinico-biological Characterization and Survival of Patients With Adult T-cell Leukemia / Lymphoma (ATL) and Patients Chronically Infected With the HTLV-1 Virus (HTLV-OBS)
ClinicalTrials.gov study NCT05237245. IPD Sharing: NO. Countries: 1. Publications: 0.
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.