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.
385
datasets available to search
ShareScore release 0.9.0
Dataset results
385 results for “COMPASS”
COMPASS Study: an Online Cognitive-behavioural Therapy (CBT) Program Treating Anxiety and Low Mood in Long-term Conditions During the COVID-19 Pandemic
ClinicalTrials.gov study NCT04535778. IPD Sharing: YES. Countries: 1. Publications: 2.
Compassion-Based Resiliency Training (CBRT) Intervention on Racism-based Stress
ClinicalTrials.gov study NCT06146218. IPD Sharing: NO. Countries: 1. Publications: 12.
Executive Functioning in TBI From Rehabilitation to Social Reintegration: COMPASS
ClinicalTrials.gov study NCT01816061. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effect of Self-Compassion Interventions on Nursing Students' Stress, Resilience, and Psychological Well-Being
ClinicalTrials.gov study NCT06754683. IPD Sharing: YES. Countries: 1. Publications: 6.
The COMPASS Study: A Study of Volanesorsen (Formally ISIS-APOCIIIRx) in Patients With Hypertriglyceridemia
ClinicalTrials.gov study NCT02300233. IPD Sharing: NO. Countries: 6. Publications: 2.
COMPASS 65+ - Community-based Physical Activity Snacks for Healthy Aging in Overweight and Obese Aged Adults
ClinicalTrials.gov study NCT07289529. IPD Sharing: NO. Countries: 1. Publications: 10.
Compass Hinge Stabilization of Knee Dislocations: A Randomized Trial
ClinicalTrials.gov study NCT00582517. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Self-compassion Therapist-led Online Group Treatment for Adolescents With Distress, Anxiety, and Depression
ClinicalTrials.gov study NCT05448014. IPD Sharing: NO. Countries: 1. Publications: 7.
Pre-consultation Compassion Among Patients Referred to a Cancer Center
ClinicalTrials.gov study NCT04503681. IPD Sharing: YES. Countries: 1. Publications: 2.
The Effects of Resilience and Self-efficacy on Nurses' Compassion Fatigue
ClinicalTrials.gov study NCT04911504. IPD Sharing: NO. Countries: 1. Publications: 18.
Chronicle Offers Management to Patients With Advanced Signs and Symptoms of Heart Failure (COMPASS-HF)
ClinicalTrials.gov study NCT00643279. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Compassion Meditation for Older Adults
ClinicalTrials.gov study NCT03964246. IPD Sharing: NO. Countries: 1. Publications: 0.
CCSH (Compassion-Centered Spiritual Health) for Teams
ClinicalTrials.gov study NCT06722027. IPD Sharing: YES. Countries: 1. Publications: 1.
Design and implementation of a brief digital mindfulness and compassion training app for health care professionals: cluster randomized controlled trial
Open the record for dataset details and reuse information.
Six-month stability of individual differences in sports coaches’ burnout, self-compassion and social support
Open the record for dataset details and reuse information.
(DATA) - Compassion and Personal Distress Depend on the Degree of Closeness to the Other in Suffering
<p>All data from our study "<strong>Compassion and Personal Distress Depend on the Degree of Closeness to the Other in Suffering"</strong></p>
Data from: Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes
Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21–36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities.
Compassion and self-care in the care sector
<p>Care, by itself, brings thoughts of warmth, compassion, and doing right by the people who need your help. As an employment Secto<strong>r</strong>, it sees life or death stakes placed on a chronically undervalued staff, working under stringent budgets in high pressure working environments.</p> <p>What, then, can we learn from the fields of trauma research and community support in caring for those carers?</p> <p>Dr Dianne Wepa of Charles Darwin University joins us again with colleagues Professor Mary Steen from the University of Northumbria and Dr Lisa Di Lemma from Liverpool Hope University to talk about self care and self compassion as tools for improving public health. </p> <p>As a note, this episode includes discussions of workplace bullying and medical trauma. Listener discretion is advised. </p> <p>Read the original research: https://doi.org/10.2174/18743501-V15-E221020-2022-39<br><br>Listen to Dr Wepa's previous episodes:</p> <ul> <li>https://doi.org/10.5281/zenodo.10213207</li> <li>https://doi.org/10.5281/zenodo.10455774</li> </ul>
Logical coherence in 2D compass codes Supplementary Material
<h1>Logical coherence in 2D compass codes</h1> <p>This repo contains the tools to reproduce and share the data for the paper "Logical Coherence in 2D Compass Codes" by Balint Pato, Will Judd Staples, and Kenneth R. Brown (2025) as well as data for B. Pato, Q. Miao and K. R. Brown, "Optimal Decoding of 2D Compass Codes Under Coherent Noise," 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 448-449, doi: 10.1109/QCE60285.2024.10349.</p> <h1>Setup</h1> <p>Python 3.x is required. The instructions for a Unix/Linux system:</p> <pre><code>cd coherence-in-compass-codes-paper python3 -m venv ~/.virtualenvs/cicc . ~/.virtualenvs/cicc/bin/activate pip install -r requirements.txt -r msim/requirements.txt -r msim/requirements.dev.txt </code></pre> <p>Before running any Python file, make sure the source folder is in PYTHONPATH, for example:</p> <pre><code>export PYTHONPATH="." </code></pre> <p>A quick end-to-end test that everything works:</p> <pre><code>check/all </code></pre> <h1>Downloading data</h1> <p>Pregenerated data is available in <code>data/codes.db</code> for the Logical Coherence in 2D Compass Codes paper.</p> <p>The number of samples per code family:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>qshor[0.166667]_13x13_0</td> <td>2,178,400</td> </tr> <tr> <td>qshor[0.166667]_17x17_0</td> <td>2,185,800</td> </tr> <tr> <td>qshor[0.166667]_21x21_0</td> <td>2,210,800</td> </tr> <tr> <td>qshor[0.166667]_9x9_0</td> <td>2,176,600</td> </tr> <tr> <td>qshor[0.333333]_13x13_0</td> <td>3,310,800</td> </tr> <tr> <td>qshor[0.333333]_17x17_0</td> <td>3,317,870</td> </tr> <tr> <td>qshor[0.333333]_21x21_0</td> <td>3,344,800</td> </tr> <tr> <td>qshor[0.333333]_9x9_0</td> <td>3,308,000</td> </tr> <tr> <td>qshor[0.5]_13x13_0</td> <td>2,381,400</td> </tr> <tr> <td>qshor[0.5]_17x17_0</td> <td>2,388,000</td> </tr> <tr> <td>qshor[0.5]_21x21_0</td> <td>2,398,600</td> </tr> <tr> <td>qshor[0.5]_9x9_0</td> <td>2,379,400</td> </tr> <tr> <td>qshor[0.666667]_13x13_0</td> <td>2,808,400</td> </tr> <tr> <td>qshor[0.666667]_17x17_0</td> <td>2,827,000</td> </tr> <tr> <td>qshor[0.666667]_21x21_0</td> <td>2,876,600</td> </tr> <tr> <td>qshor[0.666667]_9x9_0</td> <td>2,805,600</td> </tr> <tr> <td>qshor[0.833333]_13x13_0</td> <td>3,869,600</td> </tr> <tr> <td>qshor[0.833333]_17x17_0</td> <td>3,883,200</td> </tr> <tr> <td>qshor[0.833333]_21x21_0</td> <td>3,913,400</td> </tr> <tr> <td>qshor[0.833333]_9x9_0</td> <td>3,861,400</td> </tr> <tr> <td>surface_13x13</td> <td>2,004,800</td> </tr> <tr> <td>surface_17x17</td> <td>2,004,800</td> </tr> <tr> <td>surface_21x21</td> <td>2,004,800</td> </tr> <tr> <td>surface_9x9</td> <td>2,004,800</td> </tr> <tr> <td>zstackedshor[h11]_11x11_0</td> <td>612,250</td> </tr> <tr> <td>zstackedshor[h1]_11x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_5x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_7x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_9x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h2]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h5]_5x5_0</td> <td>700,000</td> </tr> <tr> <td>zstackedshor[h7]_7x7_0</td> <td>700,000</td> </tr> </tbody> </table> <p>For the surface code ML database:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>surface_101x101</td> <td>40,000</td> </tr> <tr> <td>surface_13x13</td> <td>1,599,200</td> </tr> <tr> <td>surface_151x151</td> <td>40,000</td> </tr> <tr> <td>surface_17x17</td> <td>1,599,200</td> </tr> <tr> <td>surface_201x201</td> <td>40,000</td> </tr> <tr> <td>surface_21x21</td> <td>1,599,200</td> </tr> <tr> <td>surface_23x23</td> <td>1,600,000</td> </tr> <tr> <td>surface_251x251</td> <td>40,000</td> </tr> <tr> <td>surface_25x25</td> <td>1,600,000</td> </tr> <tr> <td>surface_27x27</td> <td>1,600,000</td> </tr> <tr> <td>surface_29x29</td> <td>1,600,000</td> </tr> <tr> <td>surface_33x33</td> <td>1,600,000</td> </tr> <tr> <td>surface_37x37</td> <td>1,600,000</td> </tr> <tr> <td>surface_45x45</td> <td>2,000,000</td> </tr> <tr> <td>surface_53x53</td> <td>2,000,000</td> </tr> <tr> <td>surface_61x61</td> <td>2,000,000</td> </tr> <tr> <td>surface_69x69</td> <td>2,000,000</td> </tr> <tr> <td>surface_9x9</td> <td>1,599,380</td> </tr> </tbody> </table> <p>Sample queries:</p> <ul> <li>to find the number of samples above:</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>order</span> <span>by</span> theta_phys <span>asc</span>; </code></pre> <ul> <li>to find the number of samples per datapoint for surface codes</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>having</span> code_id <span>like</span> <span>'surface%'</span> <span>order</span> <span>by</span> theta_phys <span>asc</span> ; </code></pre> <ul> <li>to query data underlying the plots for infidelity metrics for the d=9 qshor=5/6 compass codes:</li> </ul> <pre><code><span>select</span> <span>*</span> <span>from</span> qshor_loaf <span>where</span> code_id <span>like</span> <span>'qshor%0.83%9x9%'</span>; </code></pre> <h2>Database schema</h2> <pre><code><span>-- code families are the roots of parametrized code hierarchies, e.g. surface, qshor. The convention is to also use the code_family as table names describing the parametrized members. </span> <span>CREATE</span> <span>TABLE</span> CODE_FAMILIES(CODE_FAMILY, <span>UNIQUE</span>(CODE_FAMILY)); <span>-- surface code members. Technically possible to create non-square surface codes, but in this paper we only explored square ones. </span> <span>CREATE</span> <span>TABLE</span> SURFACE(CODE_ID, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>-- families of qshor (x check density) parametrized random compass codes. </span> <span>CREATE</span> <span>TABLE</span> qshor(CODE_ID, QSHOR, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>--the main samples table, for a given code, and physical rotation angle the logical rotation angle is recorded</span> <span>CREATE</span> <span>TABLE</span> LOGICAL_ANGLE_SAMPLES(CODE_ID,THETA_PHYS TEXT, THETA_LOGICAL TEXT); <span>-- diamond distance metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_loaf(code_id, theta_phys, mean, std, num_records); <span>-- diamond distance metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor(CODE_ID, ZSHOR_HEIGHT, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>-- ML decoder version </span> <span>CREATE</span> <span>TABLE</span> qshor_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> qshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_loaf_ml(code_id, theta_phys, mean, std, num_records); </code></pre> <h1>Plots</h1> <p>Make sure that you have the data under <code>data/codes.db</code> - this needs to be a SQLite database. See the previous section on how to create it from the published data.</p> <p>Plots are generated under the folder <code>figures</code>. There are separate entry points for each figure in the paper:</p> <ul> <li>combined thresholds in the appendix resulting in <code>figures/threshold_plot-combined_full.pdf</code> and <code>figures/threshold_plot-combined_zoom.pdf</code>:</li> </ul> <pre><code>python cicc/plot/combined_threshold.py </code></pre> <ul> <li>the main plot containing the manually extracted thresholds for the random compass codes and the surface code:</li> </ul> <pre><code>python cicc/plot/qshor_family.py </code></pre> <ul> <li>the QCE2024 poster / extended abstract plots</li> </ul> <pre><code>python cicc/plot/qce2024_figures.py </code></pre> <ul> <li>the repetition code and Z-stacked Shor code plots matching the formulae plots</li> </ul> <pre><code>python cicc/plot/zstack_zshor_thresholds.py </code></pre> <h1>A note on angle conventions</h1> <p>All rotations in this project are around the Z-axis. There are two ways one can interpret the rotation angle based on the unitary rotation:</p> <ul> <li>spin angles: Rz(theta_spin) = exp(-i theta_spin/2 Z)</li> <li>direct angles: Rz(theta_direct) = exp(i theta_direct Z)</li> </ul> <p>Thus, for the same rotation unitary, theta_spin = - 2 * theta_direct. The paper by Bravyi, Engelbrecht, Konig and Peard [^bravyi2018] for the rotated surface code uses the direct angles convention, and similarly, msim uses direct angles. However, this paper uses spin angles closer to the physics convention.</p> <p>[^bravyi2018]:Bravyi, Sergey, Matthias Englbrecht, Robert König, and Nolan Peard, ‘Correcting Coherent Errors with Surface Codes’, Npj Quantum Information, 4.1 (2018), 55 <a href="https://doi.org/10.1038/s41534-018-0106-y">https://doi.org/10.1038/s41534-018-0106-y</a></p> <p>The table below summarizes the angle conventions in different parts of the codebase to avoid confusion:</p> <table> <tbody><tr> <th>Place</th> <th>convention</th> </tr> </tbody><tbody> <tr> <td>physical angles for the samplers in this project</td> <td>direct angles / pi</td> </tr> <tr> <td>msim simulator</td> <td>direct angles</td> </tr> <tr> <td>msim coeffs framework</td> <td>spin angles</td> </tr> <tr> <td>database logical angles</td> <td>direct angles</td> </tr> <tr> <td>database physical angles</td> <td>direct angles</td> </tr> <tr> <td>plots</td> <td>spin angles / pi</td> </tr> </tbody> </table> <h1>Generating your own data</h1> <h2>Random Compass Codes</h2> <p>Generating data for random compass codes for a given set of qshor values, distances and physical theta values:</p> <pre><code> python cicc/sample_angles/qshor_angles.py --help usage: qshor_angles.py [-h] [--db DB] --dz DZ --q Q --theta_phys THETA_PHYS [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --q Q Qshor probability metric --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples, which is divided across sqrt(num_runs) randomly generated codes </code></pre> <p>For example:</p> <pre><code>python cicc/sample_angles/qshor_angles.py --dz "[9, 13, 17, 21]" --theta_phys="np.linspace(0.01,0.25, 10)" --qshor="[1/6, 2/6, 3/6, 4/6, 5/6]" </code></pre> <h2>Surface code</h2> <p>Generating data for the rotated surface code:</p> <pre><code>python cicc/sample_angles/rsc_angles.py --help usage: rsc_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --batch_size BATCH_SIZE batch size for writing to the db --num_workers NUM_WORKERS parallelism --num_runs NUM_RUNS number of iterations: the total number of samples </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/rsc_angles.py --dz "[5, 9, 13, 17, 21, 25, 29]" --theta_phys="np.linspace(0.01,0.25, 10)" </code></pre> <h2>Repetition codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: repcodes_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --dz "[5,7,9,11]" --theta_phys "list(reversed(list(np.linspace(0.04,0.18,20))))" --num_runs 10000 </code></pre> <h2>Z-stacked Shor codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: zstacked_shor_angles.py [-h] [--db DB] --dz DZ [--height HEIGHT] [--zshor ZSHOR] --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] Sample angles for ZStackedShor codes. Example usage: Run 10,000 samples for height-2 and height-3 ZStackedShor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height [2,3] --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for Z-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --zshor --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for X-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height 1 --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --height HEIGHT height of the Z-Shor code block --zshor ZSHOR Z-shor code - Z-Shor height equals to dz --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <h2>Regenerating aggregated statistics</h2> <p>In the combined plot (<code>cicc/plot/combined_threshold.py</code>), by default, the already computed statistics are plotted. Hence, when new data is generated, these need to be recalculated. In <code>cicc/plot/combined_threshold.py</code> there is a section that can be modified to trigger the required recalculation (slow):</p> <pre><code> recompute( conn, <span># set to True to recompute qshor statistics </span> recompute_qshor=<span>False</span>, <span># set to True to recompute surface code statistics </span> recompute_rsc=<span>False</span>, <span># set to True to recompute qshor statistics with ML decoding </span> recompute_ml_qshor=<span>False</span>, <span># set to True to recompute surface code statistics with ML decoding</span> recompute_ml_rsc=<span>False</span>, )</code></pre>
Samples for CNIC-Proteomics Nextflow pipelines: nf-PTM-compass
<p>We present several input sample files for the execution of various Nextflow pipelines developed by the <em>Cardiovascular Proteomics Lab/Proteomics Unit at the National Centre for Cardiovascular Research</em> (CNIC, <a href="https://www.cnic.es/" target="_blank" rel="noopener">https://www.cnic.es</a>).</p> <p>The <strong>nf-PTM-compass</strong> enhances the identification and quantification of Post-Translational Modifications (PTMs) (<a href="https://github.com/CNIC-Proteomics/nf-PTM-compass" target="_blank" rel="noopener">https://github.com/CNIC-Proteomics/nf-PTM-compass</a>).</p> <p>The available sample files are as follows:</p> <ul> <li><strong>heteroplasmic_heart.zip</strong>: Heart tissue. Input files for nf-PTM-compass, derived from the study by Bagwan N, Bonzon-Kulichenko E, Calvo E, et al. (*), with results from an open search conducted using nf-SearchEngine results.</li> <li><strong>heteroplasmic_liver.zip</strong>: Liver tissue. Input files for nf-PTM-compass, derived from the same study (*), with results from an open search conducted using nf-SearchEngine results.</li> <li><strong>heteroplasmic_muscle.zip</strong>: Mucle tissue. Input files for nf-PTM-compass, derived from the same study (*), with results from an open search conducted using nf-SearchEngine results.</li> </ul> <p> </p> <p>(*) Bagwan N, Bonzon-Kulichenko E, Calvo E, et al. Comprehensive Quantification of the Modified Proteome Reveals Oxidative Heart Damage in Mitochondrial Heteroplasmy. <em>Cell Reports</em>. 2018;23(12):3685-3697.e4. doi:10.1016/j.celrep.2018.05.080</p> <p> </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.