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.
137
datasets available to search
ShareScore release 0.9.0
Dataset results
137 results for “SLAM”
GRAND-SLAM analysis of mESCs treated with 4sU for 24h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)
<p>This is the processed SLAM-seq data from the 24h 4sU data set published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse (ensembl v90). Then run:</p> <p>gedi -e Slam -double -reads slam_ameres_24h.cit -genomic m.ens90 -prefix grandslam_t15/24h -plot -progress</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j ameres_24h.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.4<br> GRAND-SLAM 2.0.7<br> STAR version 2.5.3a</p>
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
<p>This is the dataset release for the <strong><a href="https://www.sigmobile.org/mobisys/2024/" target="_blank" rel="noopener">ACM MobiSys 2024</a> </strong>paper "<strong>Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry</strong>".</p> <ul> <li><strong>Project Website:</strong> <a href="http://radarize.github.io" target="_blank" rel="noopener">https://radarize.github.io</a></li> <li><strong>Project Code:</strong> <a href="http://github.com/ConnectedSystemsLab/radarize_ae" target="_blank" rel="noopener">github.com/ConnectedSystemsLab/radarize_ae</a></li> </ul> <p>If you found this useful, please cite </p> <pre><code>@inproceedings{sie2024radarize, author = {Sie, Emerson and Wu, Xinyu and Guo, Heyu and Vasisht, Deepak}, title = {Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry}, booktitle = {The 22nd ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys '24)}, year = {2024}, doi = {https://doi.org/10.1145/3643832.3661871}, }</code></pre>
Hannover University SLAM dataset - Radar, Velodyne
<p>Dataset recorded using the Taurob Tracker in Hannover University. The data includes a novel radar sensor and velodyne data.</p>
Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM
<p>This is the example data for the protocol "Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM".</p> <p>The test data comprises sequencing data from 12 different cells. Five of them in mock condition with 4sU, five infected with MCMV with 4sU and 2 uninfected cells without 4sU. For each sample, paired-end sequencing was conducted and therefore two files exist for each sample. The files can be found at data/fastq.</p> <p>The reads should be mapped against the mouse genome and the MCMV genome, which are located in data/genome. The sequence is in fasta-fomat and the features in gtf-format.The scripts conducting quality control, read mapping and GRAND-SLAM analysis can be found in the processing folder. For each computational step in this protocol there is a shell script for running the necessary steps. Executing all scripts will reproduce the analysis of our test data</p>
GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.75, 3, 12 h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)
<p>This is the processed SLAM-seq data from the progressive labeling data set 0, 0.75, 3, 12 h published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file progressive.tsv.gz is the GRAND-SLAM output table.</p> <p><br> To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA).</p> <p>https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz</p> <p>Next index the mouse genome using the here provided mESC-end.gtf file:</p> <p>gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends</p> <p>Finally call grandslam:</p> <p>gedi -e Slam -trim5p 15 -reads progressive.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/progressive -plot -conv 0.025 -err 0.00035 -D -modelall -allGenes</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p>Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j progressive.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>To create the grandslam output with fixed parameters follow the steps above.</p> <p>gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a</p>
GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.5, 1, 3, 6, 12, and 24h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)
<p>This is the processed SLAM-seq data from the 0, 0.5, 1, 3, 6, 12, and 24h 4sU data set published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file pulse-chase.tsv.gz is the GRAND-SLAM output table. The halflives.tsv contains the half-lives from Herzog et al.</p> <p>To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA).</p> <p>https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz</p> <p>Next index the mouse genome using the provided mESC-end.gtf file:</p> <p>gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends</p> <p>Finally call grandslam:</p> <p>gedi -e Slam -trim5p 15 -chase_notrescued.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/chase_notrescued-plot -conv 0.025 -err 0.00035 -D -modelall -allGenes</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it</p> <p>You can also start from scratch (i.e., the json file):</p> <p>Prepare the mouse genome (ensembl v90) and the murine rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j chase_notrescued.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>To create the grandslam output with fixed parameters follow the steps above.</p> <p>gedi toolkit 1.0.4 GRAND-SLAM 2.0.7 STAR version 2.5.3a</p>
GRAND-SLAM analysis of increasing 4sU concentration SLAM-seq datasets for grandRescue
<p>These are processed datasets of 3 cell lines (U20S, HFF-TerT, HCT116) labeled for 1 hour with 4sU concentrations of 100µM, 200µM, 400µM and 800µM for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects.</p> <p>The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (h.ens90):</p> <p>gedi -e Slam -trim5p 15 -reads *.cit -genomic h.ens90 -prefix grandslam_t15/* -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the human genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b</p>
GRAND-SLAM analysis of simulated nucleotide conversion in Illumina TruSeq data sets for grandRescue
<p>These are processed data sets from the simulation of nucleotide conversions (T>C) in single-end and paired-end Illumina TruSeq reads for the purpose of investigating 4sU-induced mapping impairment by read lengths and library preparation methods and the potential of grandRescue to alleviate these effects.</p> <p>The original data set is from: Sarantopoulou, D. <em>et al. </em>(https://doi.org/10.1038/s41598-019-49889-1)</p> <p>GEO Accession:GSE124167 (samples: GSM3523316 - GSM3523318)</p> <p> </p> <p>The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output) for single-end (R1) and paired-end before and after rescue. The *.tsv.gz files are the GRAND-SLAM output tables.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genome:</p> <p>gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p> </p> <p>Software versions:</p> <p> gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b</p>
GRAND-SLAM analysis of simulated nucleotide conversion in QuantSeq data sets for grandRescue
<p>These are processed data sets from the simulation of nucleotide conversions (T>C) in QuantSeq reads for the purpose of investigating 4sU-induced mapping impairment by read lengths and library preparation methods and the potential of grandRescue to alleviate these effects.</p> <p>The original data set is from: Lee, J. W. <em>et al. </em>(https://doi.org/10.1038/s41586-019-1004-y)</p> <p>GEO Accession: GSE109480 (Samples: GSM2944116 – GSM2944120)</p> <p> </p> <p>The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output) before and after rescue. The *.tsv.gz files are the GRAND-SLAM output tables.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse genome. Then run the following command with the respective cit-files, prefixes (*.cit) and genome:</p> <p>gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p> </p> <p>Software versions:</p> <p> gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b</p>
SMT Formulas for Boolean Programs from SLAM
<p>This archive contains SMT formulas for boolean programs that were generated with the software verifier SLAM.</p> <p>The archive was contributed to the SV-Benchmarks repository (when it was still hosted as SVN repository) by Nikolaj Bjorner on Nov. 21, 2012. [https://github.com/sosy-lab/sv-benchmarks/commit/9a1394fe44b97c1fe8eeaea831a3627e34eb1c11]</p> <p>It is not contained directly in the Git repository at https://github.com/sosy-lab/sv-benchmarks/ because GitHub does not support files larger than 200 MB.</p>
Data for article: Neural Graph Mapping for Dense SLAM with Efficient Loop Closure
<p>Data to reproduce experiments in "Neural Graph Mapping for Dense SLAM with Efficient Loop Closure".</p> <p>It contains:</p> <ul> <li>a preprocessed version of the Kintinuous data from <a href="https://github.com/mp3guy/Kintinuous">here</a> (kintinuous_data.zip)</li> <li>novel sequences on the larger Replica scenes (replica_big_data.zip)</li> <li>ORB-SLAM2 results on <ul> <li>Kintinuous (kintinuous_slam.zip)</li> <li>Replica Big (replica_big_slam.zip)</li> <li>Replica iMAP (replica_slam.zip)</li> <li>NRGBD (nrgbd_slam.zip)</li> <li>ScanNet subset (scannet_subset_slam.zip)</li> </ul> </li> <li>Additional views for evaluation purposes as <a href="https://github.com/JingwenWang95/neural_slam_eval">proposed by Co-SLAM</a> for<br> <ul> <li>Replica Big (replica_big_data.zip)</li> <li>Replica iMAP (replica_coslam_eval.zip, modified from <a href="https://github.com/JingwenWang95/neural_slam_eval">here</a> with additional views for staircase and kitchen scenes, which were missing in the original data)</li> <li>NRGBD (nrgbd_coslam_eval.zip, from <a href="https://github.com/JingwenWang95/neural_slam_eval">here</a>)</li> </ul> </li> </ul>
Dataset for for manuscript "Short Large-Amplitude Magnetic Structures (SLAMS) at Mercury observed by MESSENGER"
<p>This data set contains event times and basic properties of events analysed in the manuscript "Short Large-Amplitude Magnetic Structures (SLAMS) at Mercury observed by MESSENGER".</p>
Dynamic SLAM
<p>Example application of DUDMAP using VolumeDeform and Boon dataset</p>
Test data for hdl_graph_slam
<p>Test data for hdl_graph_slam</p>
m6A RNA methylation orchestrates transcriptional dormancy during paused pluripotency [SLAM-seq]
GEO Series GSE228734. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing; Other.
SLAM-seq ML-792
GEO Series GSE167221. Mus musculus. 24 samples. Type: Other.
m6A sites in the coding region trigger translation-dependent mRNA decay (SLAM-seq)
GEO Series GSE273217. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Modulating Androgen Receptor-driven Transcription in Prostate Cancer with Selective CDK9 Inhibitors [SLAM-seq]
GEO Series GSE156884. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Embryonic lumenogenesis is controlled by selective mRNA decay triggered by LIN28A relocation [Slam-Seq]
GEO Series GSE169554. Mus musculus. 90 samples. Type: Expression profiling by high throughput sequencing.
MYC modulates TOP2A diffusion to promote substrate detection and activity [SLAM-seq]
GEO Series GSE311617. Homo sapiens. 6 samples. Type: Other.
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.