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.
80
datasets available to search
ShareScore release 0.9.0
Dataset results
80 results for “commands”
Lunar Command Module
A basic model of the Lunar Command Module of the NASA Apollo moon misions Source: Objaverse 1.0 / Sketchfab
Data and list of commands required to run the Mefite d'Ansanto gas dispersion case with VIGIL v1.3.7
<p>This dataset include input file to run a probabilistic hazard assessment of CO2 dispersion in the Mefite d'Ansanto area, Southern Italy. </p> <p>Alongside the input file, pre-retrieved meteorological data for the 1000 simulations run in this study are available in the folder "simulations". These can be used to reproduce the results presented in the manuscript [EDIT] by means of the VIGIL commands listed in the file "commands.txt". In this case, after the simulations.tar.gz is decompressed into the folder "simulations", the user can avoid running weather.py (see the first command in commands.txt) and should not modify nor cancel the file days_list.txt. A completely new run is instead performed if all the VIGIL scripts are executed. </p> <p>The archive "selected_outputs.tar" contains all the statistical outputs (ECDF and persistence) of the Mefite hazard study in .grd format and the related plots as produced by VIGIL. Part of these outputs have been used to produce the figures of the manuscript. </p>
Hummingbirds rapidly respond to the removal of visible light and control a sequence of rate-commanded escape maneuvers in milliseconds
<p>Hummingbirds routinely execute a variety of stunning aerobatic feats, which continue to challenge current notions of aerial agility and controlled stability in biological systems. Indeed, the control of these amazing maneuvers is not well understood. Here we examined how hummingbirds control a sequence of maneuvers within milliseconds and tested whether and when they use vision during this rapid process. We repeatedly elicited escape flights in Calliope hummingbirds and removed visible light during each maneuver at various instants and quantified their flight kinematics and responses. We show that the escape maneuvers were composed of rapidly-controlled sequential modules including evasion, reorientation, nose-down dive, forward flight, and nose-up to hover. The hummingbirds did not respond to the light removal during evasion and reorientation until a critical light-removal time; afterward, they showed two categories of luminance-based responses that rapidly altered maneuvering modules to terminate the escape. We also show that hummingbird maneuvers were rate-commanded and required no active braking, i.e., their body angular velocities were proportional to the change of wing motion patterns, a trait that likely alleviates the computational demand on flight control. This work uncovers key traits of hummingbird agility, which can also inform and inspire designs for next-generation agile aerial systems.</p>
Potential command areas of irrigation dams
<p>Irrigation will play a vital role in meeting future food demand. Irrigation reservoirs currently account for 40% of global surface water used for irrigation and play a critical role in agricultural production by providing water to overcome scarcity, buffering against climatic variability, and enabling enhanced yields and multi-cropping. Yet while there has been ongoing establishment of dams for irrigation across the globe, there is little understanding of the extent to which irrigation has actually expanded within their potential command areas (PCA) (i.e., [the area that can be irrigated using water from a particular irrigation source]) and whether some irrigation dams have not yet realized the full extent of irrigated agriculture that they can potentially support. To assess this, we combine georeferenced information on recently established dams (year 2000 onward) (including dam height and reservoir capacity) with elevation data to first delineate the extent of each recent major irrigation reservoir globally (N=441) and estimate the extent of each dam’s PCA, based on pumping capacities for multiple classes of irrigation pumps.</p>
Dataset: Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák, Pavel Čeleda.<br> <em>Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training.</em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637052">https://doi.org/10.1109/FIE49875.2021.9637052</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.11118">https://arxiv.org/abs/2112.11118</a></p> <ol> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Svabensky2021toolset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Tovar\v{n}\'{a}k, Daniel and \v{C}eleda, Pavel}, title = {{Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637052}, doi = {10.1109/FIE49875.2021.9637052}, }</code></pre> <p><strong>Structure of the repository</strong></p> <p>We share four types of content described below. Each of the four types of materials includes:</p> <ul> <li>a link to an up-to-date GitLab repository, which may contain possible future revisions and error corrections, and</li> <li>a ZIP archive here on Zenodo that serves as a snapshot of the state when the article was published.</li> </ul> <p><strong>Attached content</strong></p> <ol> <li><strong>Logging toolset.</strong> It is implemented in the form of <a href="https://www.ansible.com/">Ansible</a> roles and consists of three separate projects: for <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-bash">Bash logging</a>, <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-msf">Metasploit logging</a>, and <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-forward">log forwarding within the sandbox</a>.</li> <li><strong>Sample training.</strong> To quickly test the toolset, instantiate the exemplary cybersecurity game called <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/junior-hacker">Junior hacker training</a>. It already deploys the logging; no further setup is needed.</li> <li><strong>Dataset.</strong> The data collected with the toolset are available at <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>, which is being gradually updated with new data. Attached is a subset of this repository that includes the data analyzed in the paper.</li> <li><strong>Analytical tools.</strong> To analyze the data, you can either use the attached Python scripts, or instantiate <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol>
Fintech and Innovation-Data and Command
<p> The compressed file contains the data and commands for the paper. The data file named SampleData.dta contains the data set. The command file named Command.do contains all the execution commands. Please note that the commands interact with the data set.</p>
Tables of Ten Commandments (Chinese)
Tables of Ten Commandments (Simplified Chinese) 十诫 一、除耶和华之外,不可信仰别的神 二、禁止拜偶像 三、不可妄称耶和华的名 四、遵守安息日 五、孝敬父母 六、不可杀人 七、不可奸淫 八、不可偷盗 九、不可作假见证 十、不可贪婪 Source: Objaverse 1.0 / Sketchfab
Scripts and detailed command lines supporting: Ecological selection as drivers during early speciation: Insights from two allopatric cypress species in the Himalaya
Open the record for dataset details and reuse information.
Hummingbirds rapidly respond to the removal of visible light and control a sequence of rate-commanded escape maneuvers in milliseconds
Open the record for dataset details and reuse information.
Command Module: Apollo 11
This is the command module of Apollo 11 Source: Objaverse 1.0 / Sketchfab
FIGURE 3. Aplidium dissectum n in Shallow-water species of the genus Aplidium (Ascidiacea) from Kamchatka and Commander Islands
FIGURE 3. Aplidium dissectum n. sp. A, lava (holotype); B, colony, preserved (holotype); C, D, colony underwater (2006-07- 25-04 and 2006-07-25-01 respectively).
FIGURE 5. Aplidium eborinum n in Shallow-water species of the genus Aplidium (Ascidiacea) from Kamchatka and Commander Islands
FIGURE 5. Aplidium eborinum n. sp., underwater. A, specimen 2004-06-03-01; B, specimen 2006-07-26-02.
Subspecies and Distribution. A. l. lagopus Linnaeus, 1758 — most of the circumpolar range, in all Arctic tundra habitats. A. l. beringensis Merriam, 1902 — Russia (Commander Is). A. l. fuliginosus Bechstein, 1799 — Iceland, Greenland, Svalbard. A.l. pribilofensis Merriam, 1902 — Alaska (Pribilof Is). in Canidae
Subspecies and Distribution. A. l. lagopus Linnaeus, 1758 — most of the circumpolar range, in all Arctic tundra habitats. A. l. beringensis Merriam, 1902 — Russia (Commander Is). A. l. fuliginosus Bechstein, 1799 — Iceland, Greenland, Svalbard. A.l. pribilofensis Merriam, 1902 — Alaska (Pribilof Is).
Amharic Voice Commands
<p>This dataset contains voice commands that are recorded in the Amharic language</p>
Digital Economy, COVID-19 and Employment - Data and Commands
<p> The compressed file contains the data and commands for the paper. The data file named SampleData.dta contains the data set. The command file named Command.do contains all the execution commands. Please note that the commands interact with the data set.</p>
GPSR Commands Dataset
<p>Dataset of robot commands</p>
FIGURE 5 in Poorly known Ascidiacea collected in the vicinity of the Commander Islands and East Kamchatka, NW Pacific
FIGURE 5. Molgula beringense sp.n. A, holotype (ZMMU As-95) opened along ventral midline, branchial sac removed; B, juvenile specimen, body removed from the test.
FIGURE 6. Molgula tzetlini. A in Poorly known Ascidiacea collected in the vicinity of the Commander Islands and East Kamchatka, NW Pacific
FIGURE 6. Molgula tzetlini. A, specimen opened along ventral midline, branchial sac removed (KBPIG 1066/8); B, body removed from the test (KBPIG 725/6); C, right gonad and renal sac (KBPIG 700/3); D, gut loop and left gonad of juvenile specimen (KBPIG 723/5).
FIGURE 4 in Poorly known Ascidiacea collected in the vicinity of the Commander Islands and East Kamchatka, NW Pacific
FIGURE 4. Molgula cooperi (KBPIG 658/1). A, specimen opened along ventral midline, branchial sac removed; B, anterior part of the right gonad.
FIGURE 3. Ascidia escabanae. A in Poorly known Ascidiacea collected in the vicinity of the Commander Islands and East Kamchatka, NW Pacific
FIGURE 3. Ascidia escabanae. A, dorsal lamina; B, detail of the branchial sac; C, specimen opened along ventral midline, branchial sac removed; D, internal view of antero-dorsal region.
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.