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Data quality definitions systematic search; data and coding (2008-2024 april, version edit Dimensional-definitions worksheet)
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Search Strategies
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Search Strategy
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Live cell imaging reveals the trade-off between target search flexibility and efficiency for Cas9 and Cas12a
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Artifacts for the paper "Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search"
<p>This deposit contains measurement data and additional artifacts pertaining to the <a href="https://ieeexplore.ieee.org/abstract/document/10315708">Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search</a>" paper.</p> <p>Specifically, the artifacts are divided into 6 directories:</p> <ul> <li><em>0-config/ </em>contains configuration files use to generate input scene specifications (using Scenic) with various number of actors.</li> <li><em>1-data/</em> contains the generated input scene specifications for each map and actor size. It also contains additional scene specifications used for RQ3 (in the `zalaFullcrop/` subfolder).</li> <li><em>2-results/</em> contains measurement results (i.e. (1) visualizations of generated scenes and (2) corresponding scene specifications containing exact positions of each vehicle, as well as (3) measurement data) for each research question.</li> <li><em>3-figures/</em> contains figures derived from the contents of `results/` (including additional figures not included in the publication).</li> <li><em>4-TestingSemanticSegmentation/</em> contains artifacts related to the integration of the CARLA simulator and of three computer-vision components: <ul> <li><em> 0-sceneConfig/</em> contains configuration files used to generate scenes.</li> <li> <em>1-scenesWithExactCoordinates/ </em>contains (1) visualizations of generated scenes and (2) corresponding Scenic-compatible scene specifications containing exact positions of each vehicle.</li> <li> <em> 2-staticImages/</em> contains dashcam images for generated scenes and corresponding ground truth semantic segmentation, obtained through the CARLA integration.</li> <li> <em>3-predictions/</em> contains predicted semantic segmentation results (and their overlay on top of the corresponding dashcam image) obtained using three computer-vision components.</li> <li> <em>4-videos/</em> contains videos of generated scenes running with the default AV stack included in CARLA.</li> <li> <em>5-metrics</em> contains initial measurement data for the predicted semantic segmentation.</li> </ul> </li> <li><em>5-simulationVideos/</em> contains videos of two scenario-based test cases executed in simulation. The two scenarios are only differenciated by the initial scene, which impacts the outcome of test execution (collision vs. no-collision).</li> </ul> <p>All the code of the proposed <a href="https://github.com/ArenBabikian/Scenic">MHS-based scene concretization approach</a> is implemented as an extension to the <a href="https://github.com/BerkeleyLearnVerify/Scenic">Scenic</a> tool, which is available under the <a href="https://opensource.org/licenses/BSD-3-Clause">3-Clause BSD License</a>.</p>
All parameter files used in PIPI2 paper for all search tools
<p>All parameter files used in PIPI2 paper for all search tools</p>
Pulsars detected in the GP survey in both imaging and beamforming searches.
<p><span>S</span><span>I</span><span> </span><span>is the mean flux density of the pulsars </span><span>in Stokes I image from this work, S</span><span>V</span><span> </span><span>is the Stokes V for the pulsars that were </span><span>detected in Stokes V image in this work and S</span><span>lit</span><span> </span><span>is the low-frequency flux density </span><span>available in the literature: ’M’ stands for MWA-image detection by Murphy et al.</span><span>(2017), ’X’ stands for MWA-incoherent beam detection by Xue et al. (2017), ’S’ </span><span>stands for MWA SMART survey detections by Bhat et al. (2023b) and ’K’ stands </span><span>for LOFAR detection by Kondratiev et al. (2016).</span><span> </span><span>α</span><span> </span><span>is the spectral index of the </span><span>pulsars.</span><span> </span><span>α</span><span> </span><span>is calculated using the flux densities of the pulsars in MWA Stokes I </span><span>image (154 MHz) and the RACS Stokes I image (888 MHz).</span></p>
Data for "A learned score function improves the power of mass spectrometry database search"
<div>These data files are associated with the following publication:</div> <div> <ul> <li>Varun Ananth, Justin Sanders, Melih Yilmaz, Bo Wen, Sewoong Oh and William Stafford Noble. "<a title="biorXiv Preprint Link" href="https://www.biorxiv.org/content/10.1101/2024.01.26.577425v2" target="_blank" rel="noopener">A learned score function improves the power of mass spectrometry database search</a>". Bioinformatics (Proceedings of the ISMB). 2024.</li> </ul> </div> <div>For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:</div> <div> <ul> <li>Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6</li> </ul> </div> <div>More specifically, the following `.raw` files were downloaded:</div> <ul> <li><code>LFQ_Orbitrap_DDA_Ecoli_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Human_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Yeast_01.raw</code></li> </ul> <div>Those files can be accessed via FTP <a title="Link to ProteomeXchange: PXD028735" href="https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/" target="_blank" rel="noopener">here</a>.</div> <div>We upload here the annotated <code>.mgf</code> files created from these <code>.raw</code> files, as described in our paper.</div> <div>The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.</div> <div> <ul> <li>Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052</li> </ul> </div> <div>We include these files here, with only minor modifications to replace <code>U</code> amino acids with <code>X</code> so that all amino acids fall into Casanovo-DB's vocabulary.</div>
A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations
<h1>Project Description</h1> <p>During our research in University of West Attica (UniWA) we addressed the problem of victim detection through foliage in Search and Rescue operations. For this purpose, a dataset of respiration signal sessions in the field was collected using a proposed tool consiting of a UWB pulsed radar system, and then these data fed a machine learning tool to enhance FR's operations by providing predictions about human presence behind foliage. In addition, two anemometer sensors were used to record wind data, and a respiration belt was employed to obtain the ground truth measurements about the subject's respiration rate.</p> <p>The setup for each session was the same. The UWB radar [<a href="https://sensorlogic.ai/sensor-products">1</a>] was mounted on tripod facing the foliage, the subject was located behind the foliage wearing a respiration belt [<a href="https://www.zephyranywhere.com/">4</a>] for breath recording. On the same tripod two anemometers [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>],[<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] were placed so a comprehesive image of the wind condiditon during the session could be obtained. These sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the foliage was fixed at 1 meter. Foliage (mostly bushes and small olive trees) had length varying from 1 to 3 meters and the subject (in case of presence session) was from 0.5 to 3 meters away from the foliage. In total we never exceeded the 9.2 meters range (unambiguous range) limit of the radar.</p> <h1>Dataset Description</h1> <p>The dataset consists of 268 sessions of radar, wind and respiration belt data, of which 141 sessions correspond to human presence and 127 to human absence. Each session has a duration of 150 seconds, thus amounting to approximately 6 hours of data for human presence and approximately 5.5 hours of data for human absence.</p> <h2>Dataset Contents</h2> <p>Each session folder is given an individual name X = posixtime; this name designates the exact time (in posixtime format) when the session was started. For example, in the dataset preview below there can be seen one folder named "1688457913"; this folder corresponds to the measurement session that was initiated exactly on 1688457913 in posixtime format (in this example, X = 1688457913). Furthermore, for the "X" posixtime-named folder, there are the following subfolders and files:</p> <p>1. One subfolder named Workspaces_X, containing:</p> <ul> <li>Files named "<em>Workspace_k.mat</em>", where k the number of the created workspaces containing radar signal recording at 16 FPS.</li> <li>A file named "<em>settings.mat</em>", containing the device settings and the session's distances regarding topology.</li> <li>A file named "<em>windData_original.mat</em>", containing the original data from anemometer sensors saved from the data stream at 4 FPS, provided from a microcontroller followed RS485 protocol.</li> </ul> <p>2. Two files containing the raw data recorded from the respiration belt (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data.)</p> <ul> <li>The "<em>1_YY_MM_DD_HH_MM_general.csv</em>", contains the timestamp in datetime of the sensor and the Android device, the heart rate estimation, the mean breaths per minute and the included IMU belt sensor measurement.</li> <li>The "<em>1_YY_MM_DD_HH_MM_wave.csv</em>", contains the timestamp in datetime of the sensor and the Android device, and 18 values (FPS) of the strain gauge sensor changes from the respiration belt.</li> </ul> <p>3. A file named "<em>X.xlsx</em>", containing the concatenation of the workspaces of the radar signal.</p> <p>4. A file named "<em>windData_X.csv</em>", containing the synchronized data of anemometer sensors with radar data.</p> <p>5. A file named "<em>BeltWfm_X.xlsx</em>", containing the synchronized data of respiration belt with radar data (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data).</p> <h1>Proposed Tool COTS components</h1> <ol> <li>SLMX4 UWB pulse radar [<a href="https://sensorlogic.ai/sensor-products">1</a>]</li> <li>Wind Speed [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>] and Direction [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] sensors</li> <li>Wind data recording equipment (UART TTL to RS485 Converter, MT3608 DC/DC converter, Arduino)</li> <li>Respiration belt [<a href="https://www.zephyranywhere.com/">4</a>]</li> </ol>
From Dots to Dynamics: Searching the Complexities of Prehistoric Mobility in the Lisbon Peninsula
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Replication package for our paper "Search-based Automated Program Repair of CPS Controllers Modeled in Simulink-Stateflow"
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Supplemental material for the paper, "Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment"
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Appendix tables for Gaia: A Context-Aware Sequence Search and Discovery Tool for Microbial Proteins
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a-wis/GT-migr-forecasts: Now-casting Romanian Migration into the United Kingdom by Using Google Search Engine Data
<p>This release contains:</p> <ul> <li>data on international migration from Romania to the UK from the International Passenger Survey,</li> <li>data on interest in migration obtained from Google Trends,</li> <li>example file on how to gather data from Google Trends,</li> <li>R and Stan code to analyse these data, including updated code for producing plots as shown in the article.</li> </ul>
Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"
<p>Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"</p>
Supplementary material 1 from: Cerri J, Carnevali L, Monaco A, Genovesi P, Bertolino S (2022) Blacklists do not necessarily make people curious about invasive alien species. A case study with Bayesian structural time series and Wikipedia searches about invasive mammals in Italy. NeoBiota 71: 113-128. https://doi.org/10.3897/neobiota.71.69422
Supplementary information
Search history, Cinahl, Medline, Scopus, Psycinfo, Embase
<p>Search history, Cinahl, Medline, Scopus, Psycinfo, Embase</p>
Code, benchmarks and experiment data for the SoCS 2022 paper "Additive Pattern Databases for Decoupled Search"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks).</p> <p>sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
Supplementary material 1 from: Cerri J, Lioy S, Porporato M, Bertolino S (2022) Combining surveys and on-line searching volumes to analyze public awareness about invasive alien species: a case study with the invasive Asian yellow-legged hornet (Vespa velutina) in Italy. NeoBiota 73: 177-192. https://doi.org/10.3897/neobiota.73.80359
Complete copy of the questionnaire on Vespa velutina in English and Italian language
Supernova search with active learning in ZTF DR3
<p>Data sources for results presented in Pruzhinskaya et al., 2022.</p> <p>Results from the Active Anomaly Discovery (AAD) algorithm and the feature data set extracted from <a href="https://www.ztf.caltech.edu/ztf-public-releases.html">ZTF DR3</a> light curves.</p> <p>"log/anomalies_feature_*.txt" files contain the list of OIDs classified by the expert as anomalies, i.e. supernova candidates, for each ZTF field.</p> <p>"log/answers_feature_*.csv" files contain answers to the AAD output given by the expert in order of their appearance.</p> <p>"log/fields.csv" contains supernova statistics for each ZTF field.</p> <p>"features/" directory represent the dataset we used for supernova search in ZTF photometric data with AAD.</p> <p>"feature_*.dat" files contain object-ordered light curve feature data, every object is built on 42 feature values, which are encoded as little endian single precision IEEE-754 float (32bit float) numbers. Feature code-names are the same for all three data sets and are listed in plain text files "feature_*.name", one code-name per line. "oid_*.dat" files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers.<br> "oid_*.dat" and "feature_*.dat" have same object order, for example the first 8 bytes of "oid_796.dat" files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of "feature_796.dat" file. Note that only observations between 58194 ≤ MJD ≤ 58483 are used, see <a href="https://doi.org/10.1093/mnras/stab316">Malanchev et al. 2021</a> for features details.</p> <p>The sample Python code to access the data as Numpy arrays:</p> <pre><code class="language-python">import numpy as np oid = np.memmap('oid_796.dat', mode='r', dtype=np.uint64) with open('feature_796.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_796.dat', mode='r', dtype=dtype, shape=oid.shape) idx = np.argmax(feature['amplitude']) print('Object {} has maximum amplitude {:.3f}'.format(oid[idx], feature['amplitude'][idx]))</code></pre> <p>It should print "Object 796206400001779 has maximum amplitude 3.739"</p>
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.