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20 results for “sandbox”
Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection
<p><span>This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.</span></p> <p><span>Brief description of each of the 3 scenarios of this SUC2 are provided below.</span></p> <p><span>The datasets for the first scenario (S1) of SUC2</span><span> examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section </span><span>1.3.1</span><span> of the SUC2 supporting document. </span><span><span>The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form</span><span>.</span><span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT</span></span></p> <p><span>The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7</span><span>, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol</span><span>. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. </span><span>This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT. </span></p> <p><span>Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section </span><span>1.3.3<span></span></span><span> of the supporting document of SUC2.</span></p> <p><span>This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng<span> </span>and .csv files. <span> </span></span></p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
Dataset for KIOS CoE Sandboxing use-case SUC4 corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme (IEC 61850 GOOSE)
<p><span>The datasets reflect on two main scenarios (S1-S2) related to SUC4 - corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme. </span><span>The first scenario explores the response of the coordinated overcurrent protection when circuit breakers (CBs) are healthy, under normal operation, i.e., SUC4/S1(without attack), and the under a FDI cyberattack on IEC 61850 - GOOSE communication protocol, i.e., SUC4/S1(with FDI attack). </span>Similarly, the second scenario investigates the response of the coordinated overcurrent protection when there a mechanical failure in the CB of the downstream feeder, under normal operation, i.e., SUC4/S2(without attack), and the under a message suppresion (MS) cyber-attack on GOOSE protocol, i.e., SUC4/S2(with MS attack). Details regarding the datasets captured during the execution of each scenario (with and without attacks), including electrical measurements and network traffic, are briefly rsummarized below, while the full details are provided in the supporting documents.</p> <ul> <li><span><strong>SUC4/S1(without attack) datasets/Normal operation (without cyber-attack on GOOSE) when CBs are healthy </strong>: This dataset is related to the operation of the sandboxing use case SUC4 described in this document, which examines operation of the protection scheme in a substation using overcurrent protective relays (IEDs) in the sandboxing environment, that communicate with each other via IEC6180/GOOSE protocol. Specifically, this dataset corresponds to the first scenario (S1) of SUC4, without any attack. More details about the scenario related to this dataset can be found in Section 1.3.1 of the SUC4 supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.</span></li> <li><span><strong>SUC4/S1(with FDI attack) datasets/FDI cyber-attack on GOOSE signals when CBs are healthy</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(without attack) datasets/ Normal operation (without attack on GOOSE) when CB presents a failure</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of<br>the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(with MS attack) datasets/MS cyber-attack on GOOSE signals when CB presents a failure</strong>: This dataset corresponds to the second scenario (S2) of SUC4, where an MS cyber-attack is conducted in the local network in order prevent critical benign messages, such inter-trip messages requesting backup protection, to reach their destination (back-up IED) when a CB failure occurs during a short-circuit event. As a result, the duration of a short-circuit is prolonged or the protection scheme is not able to clear the short-circuit event, which can cause catastrophic failures to power system. More details about the scenario related to<br>this dataset can be found in Section 1.3.2 of the support document. The dataset includes electrical measurements of the upstream and downstream feeders of<br>the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> </ul>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
FIGURE 14 in NSB (Neptune Sandbox Berlin): An expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy
FIGURE 14. Histogram of publications using or about NSB, spread by Clarivate Analytics' Impact Factor (JIF, 2017).
FIGURE 4 in NSB (Neptune Sandbox Berlin): An expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy
FIGURE 4. Result page for microfossil occurrences (the specific query here was for radiolarians from samples between 20 and 25 Ma).
Datasets for sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer
<p><span>These datasets reflect two main scenarios (S1-S2) associated to the operation of a sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer. Details about are illustrated in Section 1.3 of the supporting document. These scenarios analyse the operation of the digital twin of the IEEE 9-bus system and the differential protection scheme under healthy conditions, cyber-attack on communication channels of IEC 61850 Sample Values (SVs) protocol, and a fault in HV side of a transformer in the power system. The scenarios are presented with selected time-series plots in Section 1.3, accompanied a detailed analysis of the processes included and an impact assessment. Thus, d</span><span>uring execution of each scenario, data such as electrical measurements were captured and are collected</span> in the form of the datasets presented here.</p> <p>Specifically, </p> <ul> <li>SUC3/S1 <strong>Differential protection operation during transformer fault</strong> corresponds to the dataset of first scenario (S1) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines the operation of differential protection scheme (implemented in Typhoon controller) for a HV/MV transformer. The protection scheme receives data sent through IEC 61850 SVs from the two sides of the transformer. Specifically, this dataset corresponds to the first scenario (S1) of SUC3, where a short-circuit occurred on the HV side of a HV/MV transformer of the system. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the sinusoidal measurements were recorder by OPAL-RT.</li> <li>SUC3/S2 <strong>MITM with FDI cyber-attack in the SVs of HV transformer side</strong> corresponds to the dataset of the second scanario (S2) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines a MITM with FDI cyber-attack is conducted on the measurements of the HV side of the transformer, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the differential protection scheme via IEC 61850 protocol. Section 1.3.1 of the supporting document provides more details about the scenario related to this<br>dataset. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the measurements from the sine waves were recorder by OPAL-RT.</li> </ul>
Data for the paper "Policy Sandboxing: Empathy As An Enabler Towards Inclusive Policy-Making"
<p>Data collected and used for the analysis for the paper "Policy Sandboxing: Empathy As An Enabler Towards Inclusive Policy-Making".</p> <p>Each value - beside the demographics and the opinions - corresponds to a value of a 5-level likert scale. For a more detailed explanation please refer to the paper.</p>
Sandbox with snow [3D Scan;Low Poly]
# Sandbox with snow * Photogrammetry * PBR * Low-Poly * 8K textures * Free to use. ;) * P.S. If you like my model, please, check my [ArtStation](https://www.artstation.com/mrunity) page too :) Source: Objaverse 1.0 / Sketchfab
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Oulu facility
<p>This data set covers core network and end-to-end measurements at the Oulu facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Malaga facility
<p>This data set covers core network and end-to-end measurements at the Malaga facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Berlin facility
<p>This data set covers core network and end-to-end measurements at the Berlin facility, part of the 6G-SANDBOX infrastructure.</p>
Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Athens facility
<p>This data set covers core network and end-to-end measurements at the Athens facility, part of the 6G-SANDBOX infrastructure.</p>
Dataset for the paper Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification
<p><strong>Short Description: </strong>This is the dataset for the paper "Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification", accepted for publication in the Journal of Systems and Software. </p> <p><strong>Link to this repository: </strong><a href="https://github.com/droidxp/paper-replication-package">https://github.com/droidxp/paper-replication-package</a></p> <p><strong>Authors of the Paper</strong></p> <ul> <li>Francisco Handrick da Costa</li> <li>Ismael Medeiros</li> <li>Thales Menezes</li> <li>João Victor da Silva</li> <li>Ingrid Lorraine da Silva</li> <li>Rodrigo Bonifácio</li> <li>Krishna Narasimhanb</li> <li>Márcio Ribeiro</li> </ul> <p><strong>Abstract</strong></p> <p>The popularization of the Android platform and the growing number of Android applications (apps) that manage sensitive data turned the Android ecosystem into an attractive target for malicious software. For this reason, researchers and practitioners have investigated new approaches to address Android's security issues, including techniques that leverage dynamic analysis to mine Android sandboxes. The mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app. This exploratory phase records all calls to sensitive APIs. Later, we can use this information to (a) prevent calls to other sensitive APIs (those not recorded in the exploratory phase) or (b) run the dynamic analysis tools again in a different version of the app. During this second execution of the fuzzing tools, a warning of possible malicious behavior is raised whenever the new version of the app calls a sensitive API not recorded in the exploratory phase.</p> <p>The use of a mining sandbox approach is an effective technique for Android malware analysis, as previous research works revealed. Particularly, existing reports present an accuracy of almost 70% in the identification of malicious behavior using dynamic analysis tools to mine android sandboxes. However, although the use of dynamic analysis for mining Android sandboxes has been investigated before, little is known about the potential benefits of combining static analysis with a mining sandbox approach for identifying malicious behavior. Accordingly, in this paper we present the results of two studies that investigate the impact of using static analysis to complement the performance of existing dynamic analysis tools tailored for mining Android sandboxes, in the task of identifying malicious behavior.</p> <p>In the first study we conduct a non-exact replication of a previous study (hereafter BLL-Study) that compares the performance of test case generation tools for mining Android sandboxes. Differently from the original work, here we isolate the effect of an independent static analysis component (DroidFax) they used to instrument the Android apps in their experiments. This decision was motivated by the fact that DroidFax could have influenced the efficacy of the dynamic analyses tools positively---through the execution of specific static analysis algorithms DroidFax also implements. In our second study, we carried out a new experiment to investigate the efficacy of taint analysis algorithms to complement the mining sandbox approach previously used to identify malicious behavior. To this end, we executed the FlowDroid tool to mine the source-sink flows from benign/malign pairs of Android apps used in previous research work.</p> <p>Our study brings several findings. For instance, the first study reveals that DroidFax alone (static analysis) can detect 43.75% of the malwares in the BLL-Study dataset, contributing substantially in the performance of the dynamic analysis tools in the BLL-Study. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, with a performance similar to that reached by dynamic analysis tools.</p> <p> </p>
No Algorithmization without Representation: Pilot Study on Regulatory Experiments in an Exploratory Sandbox
<p>Accompanying data and scripts for the paper.</p>
FIGURE 2 in NSB (Neptune Sandbox Berlin): An expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy
FIGURE 2. Current architecture of the NSB database.
FIGURE 11 in NSB (Neptune Sandbox Berlin): An expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy
FIGURE 11. Age model selection in NSB_ADP_wx.
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells
GEO Series GSE194122. Homo sapiens. 38 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Exercise 2 Create a record in Zenodo Sandbox
<p>Exercise 2 Create a record in Zenodo Sandbox</p>
Data set for "Accretion cycles, structural evolution, and thrust activity in accretionary wedges with various décollement configurations: Insights from sandbox analog modeling"
<p>This data set is associated with the paper, ``Accretion cycles, structural evolution, and thrust activity in accretionary wedges with various décollement configurations: Insights from sandbox analog modeling'' by Atsushi Noda, Fabien Graveleau, Cesar Witt, Frank Chanier, and Bruno Vendeville. It is currently available on doi:PREPRINT and to be submitted to the Journal of Geophysical Research, Solid Earth. When citing this data release, please cite as well the paper.</p> <p>Contact: Atsushi Noda, a.noda@aist.go.jp</p> <p>The provided zip file contains the data below.</p> <p>DIC ---- image: Photo images used for DIC analysis<br> |<br> |-- ROI: Image files of "Region of interest" for DIC analysis<br> |<br> |-- matdata: MATLAB data files produced by Ncorr DIC analysis<br> |<br> |-- plot: Plots produced from the MATLAB data files<br> |<br> |-- movie: Images of plots of DIC analysis<br> |<br> |-- data: Output data generated by python scripts</p> <p>figure ---- Output pdf files produced by python scripts</p> <p>Geometry ---- plot: plots for geometrical parameters<br> |<br> |-- qgis: geometrical data files (shapefile format)</p> <p>RAW ---- image: RAW photo images for all models<br> |<br> |-- movies: Images of photos for all models</p> <p>script ---- ncorr_plot_eng.py: To plot the kinematic parameters from MATLAB data file<br> |<br> |-- ncorr_plot_all.py: To plot summary diagrams for the kinematic parameters<br> |<br> |-- envelope.py: To plot theoretical taper angles<br> |<br> |-- comp_wux.py: To calculate and plot strain ratios<br> |<br> |-- plot_param.py: To plot geometrical parameters<br> |<br> |-- calc_strength.py: To calculate and plot the wedge/detachment strengths <br> </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.