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9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “robust statistics”
Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation
<p>This archive contains the simulated collections, their diagnosis data, and the estimates of accuracy. For the full code and description, please refer to https://github.com/julian-urbano/irj2015-reliability</p>
Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study
<p><strong>Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.</strong></p> <p>Contents:<br> 1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;<br> 2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;<br> 3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.</p> <p> </p> <p><strong>Installation guide:</strong></p> <p> 1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p> 4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p> 5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html</p>
Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs [Whole-transcriptome bisulfite sequencing]
GEO Series GSE81823. Mus musculus. 16 samples. Type: Methylation profiling by high throughput sequencing.
Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs [mRNA bisulfite sequencing]
GEO Series GSE81824. Mus musculus. 3 samples. Type: Methylation profiling by high throughput sequencing.
Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs
GEO Series GSE81825. Mus musculus. 19 samples. Type: Methylation profiling by high throughput sequencing.
robust statistical modeling improves sensitivity of high-throughput rnA structure probing experiments
GEO Series GSE78208. Saccharomyces cerevisiae. 4 samples. Type: Other.
Detection of statistically robust interactions from diverse RNA-DNA ligation data [RADICL-seq]
GEO Series GSE273203. Homo sapiens. 4 samples. Type: Other.
Detection of statistically robust interactions from diverse RNA-DNA ligation data [nucRNA-seq]
GEO Series GSE273207. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Detection of statistically robust interactions from diverse RNA-DNA ligation data [ATAC-seq]
GEO Series GSE273205. Homo sapiens. 6 samples. Type: Genome binding/occupancy 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.