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375 results for “compactness”
Development of compact transcriptional effectors using high-throughput measurements in diverse contexts
<p>HT-recruit and CRISPR HT-recruit processed datasets </p> <p>Abstract: <span>Transcriptional effectors are protein domains known to activate or repress gene expression, however, a systematic understanding of which effector domains regulate transcription robustly across genomic, cell-type, and DNA-binding domain (DBD) contexts is lacking. Here, we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes, and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library containing 114,288 sequences tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator NFZ by combining several domains, which enables efficient CRISPRa with better viral delivery, and inducible control of CAR T-cells. Together, this effector-by-context functional map reveals context-dependence across human effectors and guides effector selection for manipulating transcription.</span></p>
IAM_COMPACT_Study_7_Behavioural_changes
<p>This dataset contains the underling raw data of IAM COMPACT "Study 7 - Behavioural changes".</p> <p>The study addresses the impact of behavioural change on diets, buildings, and mobility <span>(Frilingou et al., 2024)</span>. This study aims to understand the economic ramifications arising from shifts in behaviour and social innovation within the context of the energy transition as conceptualized in Task 5.5, emphasizing the relevance of behavioural change as an added social value resulting from policy implementation, rather than solely focusing on cost or effectiveness evaluation. </p> <p>Initially, study 7 carries out a contextualisation of behavioural change by a literature review and an analysis of the behaviour-related policy representation in IAM COMPACT models (based on D4.1 methodology application). Next, it collects and processes stakeholders’ questions and propositions on behavioural changes and classifies them according to sectors and models which can respond to them. Finally, four scenarios are created from applying different policies, yielding outputs in the fields of energy, climate and land.</p> <p>Results of the study have been documented in D5.6 - Behaviour, social and disruptive innovation (DOI <a href="../doi/10.5281/zenodo.12751959">10.5281/zenodo.12751959</a>)</p>
Solution of convection equation with compact scheme different sub-domain closures.
<p>Solution of convection equation with CFL number, Nc = 0.001. Computational domain extends from -1 to 19 and from -1 to +1 in x- and y- directions; 2001\times201 equidistant points are sub divided into 10x2 processors. The solid black lines indicate the processor boundary. RK4-OUCS3 scheme is used with CD8 sub-domain closure in the top frame and NOHAP closure is used in the bottom frame</p>
Test sets and results for paper 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning'
<p>This folder contains the input data (signal-to-noise ratio time series for gravitational wave detections) and results (sky localization areas) obtained from the deep learning based sky localization model 'GW-SkyLocator', and the rapid online gravitational wave sky localization tool 'BAYESTAR' on a set of injections of gravitational wave signals from compact binary mergers.</p> <p>For details of the work, please refer to the paper, 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning' (https://arxiv.org/abs/2207.14522).</p>
Dataset for Runaway and Hypervelocity Stars from Compact Object Encounters in Globular Clusters
<p>The dataset used for Cabrera & Rodriguez 2023. If building from the showyourwork-enabled GitHub, the file can be directly unzipped into the <code>src/data</code> folder.</p> <p>The top-level contains folders for <code>CMC</code> model- and Milky Way globular cluster-delineated data (<code>cmc</code> and <code>mwgcs</code>, respectively), and some auxiliary files, including the composite catalogs. Within the two folders are subfolders for each of the <code>CMC</code> models and MWGCs. The <code>CMC</code> model folders contain some of the output files from the <code>CMC Cluster Catalog</code> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..247...48K">(Kremer+20</a>) (used in this project to examine GC evolution), and also two files <code>output_N-10.txt</code> (which has data for all <code>Fewbody</code> realizations for the model) and <code>output_N-10_ejections.txt</code> (which has data for all ejections from the model). The columns in the former file are regrettably not labeled, but correspond to the following parameters, many of which are direct <code>Fewbody</code> arguments:</p> <table align="center"> <thead> <tr> <th scope="col">#</th> <th scope="col">Parameter</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>time</td> <td>Physical time of encounter in <code>CMC </code>model</td> <td>Myr</td> </tr> <tr> <td>2</td> <td>b</td> <td>Impact parameter</td> <td>a1</td> </tr> <tr> <td>3</td> <td>vinf</td> <td>Velocity of single object at infinity</td> <td>v_crit</td> </tr> <tr> <td>4</td> <td>a1</td> <td>Initial semi-major axis of binary</td> <td>AU</td> </tr> <tr> <td>5</td> <td>e1</td> <td>Initial binary eccentricity</td> <td>-</td> </tr> <tr> <td>6</td> <td>vesc</td> <td>Local escape velocity in <code>CMC </code>model</td> <td>km/s</td> </tr> <tr> <td>7</td> <td>m10</td> <td>Mass of first binary component</td> <td>Msun</td> </tr> <tr> <td>8</td> <td>m11</td> <td>Mass of second binary component</td> <td>Msun</td> </tr> <tr> <td>9</td> <td>m0</td> <td>Mass of single object</td> <td>Msun</td> </tr> <tr> <td>10</td> <td>r10</td> <td>Radius of first binary component</td> <td>Rsun</td> </tr> <tr> <td>11</td> <td>r11</td> <td>Radius of second binary component</td> <td>Rsun</td> </tr> <tr> <td>12</td> <td>r0</td> <td>Radius of single object</td> <td>Rsun</td> </tr> <tr> <td>13</td> <td>k10</td> <td>BSE k-type of first binary component</td> <td>-</td> </tr> <tr> <td>14</td> <td>k11</td> <td>BSE k-type of second binary component</td> <td>-</td> </tr> <tr> <td>15</td> <td>k0</td> <td>BSE k-type of single object</td> <td>-</td> </tr> <tr> <td>16</td> <td>s</td> <td>Random seed for <code>Fewbody</code></td> <td>-</td> </tr> <tr> <td>17</td> <td>type_i</td> <td> <p>Index classifying initial system (see below)</p> </td> <td>-</td> </tr> <tr> <td>18</td> <td>type_f</td> <td>Index classifying final system (see below)</td> <td>-</td> </tr> <tr> <td>19</td> <td>v_crit</td> <td>Critical velocity of encounter (<a href="https://ui.adsabs.harvard.edu/abs/2004MNRAS.352....1F/abstract">Fregeau+04</a>)</td> <td>km/s</td> </tr> <tr> <td>20</td> <td>a_fin</td> <td>Final semi-major axis of binary (0 if no binary is present)</td> <td>AU</td> </tr> <tr> <td>21</td> <td>e_fin</td> <td>Final binary eccentricity (identically 0 if no binary is present)</td> <td>-</td> </tr> <tr> <td>22</td> <td>Lx</td> <td>x-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>23</td> <td>Ly</td> <td>y-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>24</td> <td>Lz</td> <td>z-component of the initial angular momentum of the system</td> <td>code</td> </tr> <tr> <td>25</td> <td>Lbinx</td> <td>x-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>26</td> <td>Lbiny</td> <td>y-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>27</td> <td>Lbinz</td> <td>z-component of the angular momentum of the initial binary</td> <td>code</td> </tr> <tr> <td>28</td> <td>Ei</td> <td>Initial energy of the encounter</td> <td>code</td> </tr> <tr> <td>29</td> <td>DeltaEfrac</td> <td>Fractional change in energy by the time of termination</td> <td>-</td> </tr> <tr> <td>30/34/38</td> <td>vfin0/1/2</td> <td>Final velocity of the final top-level object with<code> Fewbody </code>index 0/1/2 (<a href="https://ui.adsabs.harvard.edu/abs/2004MNRAS.352....1F/abstract">Fregeau+04</a>)</td> <td>v_crit</td> </tr> <tr> <td>31/35/39</td> <td>kf0/1/2</td> <td>BSE k-type of the same</td> <td>-</td> </tr> <tr> <td>32/36/40</td> <td>Rmin0/1/2</td> <td>Minimum distance between this object and any other object during the encounter</td> <td>AU</td> </tr> <tr> <td>33/37/41</td> <td>Rmin_j0/1/2</td> <td><code>Fewbody </code>index of the other object at closest passage</td> <td>AU</td> </tr> </tbody> </table> <p>The initial encounter classifying indices are as follows, where "C" denotes a compact object and "S" a star:</p> <table align="center"> <thead> <tr> <th scope="col">type_i</th> <th scope="col">Configuration</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>(C,C)+S</td> </tr> <tr> <td>2</td> <td>(C,S)+C</td> </tr> <tr> <td>3</td> <td>(C,S)+S</td> </tr> <tr> <td>4</td> <td>(S,S)+C</td> </tr> </tbody> </table> <p>The final encounter classifying indices are as follows, using the initial <code>Fewbody </code>object indices to specify if objects end up in a binary ((a,b)) or if they merge (a:b) (note that <code>Fewbody</code> initializes all binary-single encounters in the <code>type_f=3</code> configuration, i.e. the object index 0 is assigned to the single):</p> <table align="center"> <thead> <tr> <th scope="col">type_f</th> <th scope="col">Configuration</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>0+1+2 (Ionization)</td> </tr> <tr> <td>1</td> <td>(0,1)+2</td> </tr> <tr> <td>2</td> <td>(0,2)+1</td> </tr> <tr> <td>3</td> <td>0+(1,2)</td> </tr> <tr> <td>4</td> <td>0:1+2</td> </tr> <tr> <td>5</td> <td>0:2+1</td> </tr> <tr> <td>6</td> <td>1:2+0</td> </tr> <tr> <td>7</td> <td>0:1:2</td> </tr> <tr> <td>-2</td> <td>binary</td> </tr> <tr> <td>-3</td> <td>hierarchical triple</td> </tr> </tbody> </table> <p>The columns in <code>output_N-10_ejections.txt</code> are labeled, and use many of the same headers in the first table; the object indices for fields 30-41 are dropped because each row in this file corresponds to an escaper. The two additions are <code>mf</code> and <code>rf</code>, which indicate the mass and radius of the ejected object.</p> <p>Each of the <code>mwgcs</code> folders contain the FITS files described in Appendix B of the text. There are also <code>output_N-10_ejections.txt</code> files similar to the ones for the <code>CMC</code> models; the MWGC versions contain the additional fields below:</p> <table align="center"> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>vout</td> <td>Velocity of object at the time of ejection from the representative model</td> <td>km/s</td> </tr> <tr> <td>X/Y/Z</td> <td>Galactocentric X/Y/Z coordinate of object at the present day</td> <td>kpc</td> </tr> <tr> <td>U/V/W</td> <td>Galactocentric U/V/W velocity of object at the present day</td> <td>km/s</td> </tr> </tbody> </table> <p> </p>
Dataset and code: Compact module for complementary-channel THz pulse slicing
<p>Dataset and processing files for the data presented in manuscript "Compact module for complementary-channel THz pulse slicing"</p>
Evaluation of Safety and Efficacy of Bixdo Ultra Compact Water Flosser
ClinicalTrials.gov study NCT06352645. IPD Sharing: NO. Countries: 1. Publications: 25.
Arimidex: Compliance and Arthralgias in Clinical Therapy (COMPACT)
ClinicalTrials.gov study NCT00857012. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Endometrial Compaction and Its Influence on Pregnancy Rate in Frozen Embryo Cycle Regimes
ClinicalTrials.gov study NCT04454749. IPD Sharing: UNDECIDED. Countries: 1. Publications: 13.
Impact of Endometrial Compaction in Euploid Frozen Embryo Transfers
ClinicalTrials.gov study NCT04330066. IPD Sharing: NO. Countries: 1. Publications: 1.
The Impact of Endometrial Compaction on Assisted Reproductive Technology Outcome
ClinicalTrials.gov study NCT04721522. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
COMPACT - COMbining Plasma-filtration and Adsorption Clinical Trial
ClinicalTrials.gov study NCT00332371. IPD Sharing: Not stated. Countries: 1. Publications: 1.
the Effect of Endometrial Compaction Caused by Progesterone Effect on Pregnancy Outcomes
ClinicalTrials.gov study NCT04733235. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Physical simulation of the influence of the original rock strength on the compaction characteristics of caving rock in longwall goaf
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Data from: Compact cities or sprawling suburbs? optimising the distribution of people in cities to maximise species diversity
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Data from: The inverted U-shaped effect of urban hotspots spatial compactness on urban economic growth
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Biological experimental raw data from: The Incubascope : A simple, compact and large field of view microscope for long-term
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A method for determining the origin of crude drugs derived from animals using MinION, a compact next-generation sequencer
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Predicting the Long-Term Stability of Compact Multi-Planet Systems
<p>REBOUND N-body simulation archives used to train and test the machine learning models in Tamayo et al. (2020), 'Predicting the Long-Term Stability of Compact Multi-Planet Systems'. See https://github.com/dtamayo/spock for scripts to process the data, train and test the SPOCK XGBoost model.</p>
Data from: An experimental study of the influence of lithology on compaction behaviour of broken waste rock in coal mine backfill
The research aims to explore the influences of lithology on the compaction behaviours of broken waste rocks. For this purpose, a WAW1000D servo test machine and a self-made bidirectional loading test system for granular materials were used to conduct axial and lateral compaction tests on four typical types of broken waste rocks: sandstone, mudstone, limestone, and shale. On this basis, we analysed the relationships between lateral and axial stress with the strain in, and porosity of, the four types of broken waste rocks. In addition, the relationship of axial stress with lateral stress and lateral pressure coefficient, and the changes in the particle size distribution of broken waste rocks before, and after, compaction were discussed. The test results demonstrated that the samples of higher strength were found to have low lateral and axial strains as well as a lower porosity in axial and lateral loading tests; while samples of lower strength showed low lateral stress and lateral pressure coefficient under axial load. After being compacted, the samples of the four types of broken waste rocks were found to have a higher proportion of small particles, indicating some particle crushing. Moreover, the samples of lower strength were broken to a greater extent.
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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.