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2,326 results for “clusters”
Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis
<p>Datasets for study 1 & 2 for the manuscript Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis</p>
Regional and fine-scale local adaptation in salinity tolerance in Daphnia inhabiting contrasting clusters of inland saline waters
<p>Freshwater salinisation is an important threat to biodiversity, ecosystem functioning, and the provision of ecosystem services. Therefore, understanding the capacity of species to adapt to salinity gradients is crucial. Clusters of naturally saline habitats represent ideal test cases to study the extent and scale of local adaptation to salinisation. We studied local genetic adaptation of the water flea Daphnia magna, a key component of pond food webs, to salinity in two contrasting landscapes - a dense cluster of sodic bomb crater ponds and a larger-scale cluster of soda pans. We show regional differentiation in salinity tolerance reflecting the higher salinity levels of soda pans versus bomb crater ponds. We found local adaptation to differences in salinity levels at the scale of tens of metres among bomb crater pond populations but not among geographically more distant soda pan populations. The population-level salinity tolerance range was reduced in more saline bomb crater ponds through an upward shift of the minimum salt tolerance observed across clones and a consequent gradual loss of less tolerant clones in a nested pattern. Our results show genetic adaptation to salinity gradients at different spatial scales and fine-tuned local adaptation in neighbouring habitat patches in a natural landscape.</p>
A supervised Graph-based deep learning algorithm to detect and quantify clustered particles
<p>In this data repository, we provide the necessary data for replicating results, including both simulated and biological datasets. Additionally, the repository includes trained models to infer from these datasets.</p>
Data and geometries for "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory"
<p>Geometries and raw and processed data for the paper "Understanding X-ray absorption in liquid water using<br>triple excitations in multilevel coupled cluster theory"</p> <p>This work has received funding from the European Research Council (ERC)<br>under the European Union’s Horizon 2020 Research and Innovation Program<br>(grant agreement no. 101020016 and 860553), the Research Council of Norway through FRINATEK (project no. 275506), the Swedish Research Council (grant agreement no. 2021-04521), the Independent Research Fund Denmark--Natural Sciences, DFF-RP2 (grant no. 7014-00258B)<br>Computing resources from UNINETT Sigma2—the National Infrastructure for High Performance Computing<br>and Data Storage in Norway (project no. NN2962k),<br>from DeIC—Danish Infrastructure Cooperation (grant no. DeiC-DTU-N3-2023027), and from the Swiss National Supercomputing Centre (project ID uzh1).</p>
Design and implementation of a brief digital mindfulness and compassion training app for health care professionals: cluster randomized controlled trial
<p><strong>Background: </strong>Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout.</p> <p><strong>Objective:</strong> In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app–based methods may have the potential to enhance self-compassion and mindfulness in health care professionals.</p> <p><strong>Methods: </strong>In this study, we designed and implemented a scalable, digital app–based, brief mindfulness and compassion training program called "WellMind" for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated preand postintervention neural activity in core brain networks using electroencephalography source imaging as an objective<br>neurophysiological training outcome.</p> <p><strong>Results:</strong> Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=–0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=–0.368; P=.04).</p> <p><strong>Conclusions:</strong> Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention.</p>
The CluMPR Galaxy Cluster Catalogue for DESI Legacy Survey DR9
<p>Galaxy cluster catalog and cluster member galaxy catalogs compiled using the CluMPR cluster-finding algorithm. </p> <p>Paper decribing the CluMPR algorithms and cluster catalogs: The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster catalogue (M. J. Yantovski-Barth et al.)</p> <p>File description: </p> <p>DESI_clusters_2024_simple contains the official cluster catalog,</p> <p>DESI_clusters_2024_extended is the official cluster catalog + some clusters which were flagged and removed,</p> <p>north_members_reweighted contains the member galaxies for clusters in the north region of DESI Legacy Survey,</p> <p>south_members_reweighted contains the member galaxies for clusters in the south region of DESI Legacy Survey.</p>
STORM Data: Transcriptionally active chromatin loops contain both 'active' and 'inactive' histone modifications that exhibit exclusivity at the level of nucleosome clusters
<p>The dataset underlying the SMLM STORM super-resolution images of 'Transcriptionally active chromatin loops contain both ‘active’ and ‘inactive’ histone modifications that exhibit exclusivity at the level of nucleosome clusters'. See Biorxiv paper for details on sample preparation: <a href="https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf">https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf</a>, Pyranose Oxidase STORM buffer on Elyra 7 Zeiss Microscope, processed with Zen Black. Samples are named according to which figures they occur in the above paper.</p>
FLAMINGo, an EnLight EVs Cluster member
<p>FLAMINGo together with LEVIS, REVOLUTION, Fatigue4light and ALMA projects created a collaborative cluster team namely "EnLight EVs" <br><br>*This Video was developed with the support of the Horizon results booster Portfolio Dissemination Strategy*</p>
Data for: Tomato root specialized metabolites evolved through gene duplication and regulatory divergence within a biosynthetic gene cluster
<p>Tremendous plant metabolic diversity arises from phylogenetically-restricted specialized metabolic pathways. Specialized metabolites are synthesized in dedicated cells or tissues, with pathway genes sometimes colocalizing in biosynthetic gene clusters (BGCs). However, the mechanisms by which spatial expression patterns arise and the role of BGCs in pathway evolution remain underappreciated. In this study, we investigated the mechanisms driving acylsugar evolution in the Solanaceae. Previously thought to be restricted to glandular trichomes, acyl sugars were recently discovered in cultivated tomato roots. We demonstrated that acyl sugars in cultivated tomato roots and trichomes have different sugar cores, identified root-enriched paralogs of trichome acyl sugar pathway genes, and characterized a key paralog required for root acyl sugar biosynthesis, <em>SlASAT1-LIKE</em> (<em>SlASAT1-L</em>), which is nested within a previously-reported trichome acyl sugar BGC. Finally, we provided evidence that <em>ASAT1-L</em> arose through duplication of its paralog, <em>ASAT1</em>, and was trichome-expressed before acquiring root-specific expression in the <em>Solanum</em> genus. Our results illuminate the genomic context and molecular mechanisms underpinning metabolic diversity in plants.</p>
Datasets for "Micromonosporaceae Biosynthetic Gene Cluster Diversity Highlights the Need for Broad Spectrum Investigation"
<p>In this data collection is:<br><strong>Data S1</strong>: A folder with all the fasta files, representing the 42 strains (41 <em>Micromonosporaceae</em>, 1 <em>Streptomycetaceae</em>).<br><strong>Data S2</strong>: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v5.1.1. These files were used as inputs for BiG-SCAPE and BiG-SLiCE.<br><strong>Data S3</strong>: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v6.1.0.<br><strong>Data S4</strong>: A folder containing all the Quast outputs for the 42 strains.<br><strong>Data S5</strong>: A folder containing all the BUSCO outputs for the 42 strains. Example scripts are provided for scraping relevant information from the individual BUSCO outputs.<br><strong>Data S6</strong>: A folder containing GTDB (Genome Taxonomy Database) classification results, and species-level grouping results using FastANI (95% cutoff).<br><strong>Data S7</strong>: A folder containing an Interactive Tree of Life (iTOL)-compatible bar chart annotation using antiSMASH v5.1.1 BGC region information.<br><strong>Data S8</strong>: A folder containing a word document that describes the parameters used with Ubuntu WSL (Windows Subsystem for Linux) on the command line for programs antiSMASH v6.1.2, BiG-SCAPE v1.1.2, and BiG-SLiCE v1.1.1. Also included are parameters for MDSC in python. An example script is also provided for batch queries of BGCs against BiG-SLiCE v1.1.1’s pre-processed dataset of ~1.2 million BGCs.<br><strong>Data S9</strong>: A folder containing the BiG-SCAPE visualization of the 38 <em>Micromonosporaceae</em> (post-QC filtering, excluding WMMA1363, WMMB482, WMMB486, and WMMC500) in Cytoscape.<br><strong>Data S10</strong>: A folder containing:<br>The pre-processed dataset of 1.2 million BGCs from BiG-SLiCE.<br>All report folders generated by BiG-SLiCE for the 779 <em>Micromonosporaceae </em>BGCs queried against the 1.2 million BGCs.<br>The results data.db and associated folders for the pre-processed dataset of 1.2 million BGCs.<br><strong>Data S11</strong>: A folder containing the scripts necessary to regenerate the figures and perform independent analyses, and the relevant data used for the analyses.<br><strong>Data S12: </strong>A folder containing the results of the nucleotide blast of WMMA1947.region12's siderophore contig against WMMD1120.region14's siderophore contig.</p> <p><strong>Supplementary Information: </strong>Supplementary Table S1 and Supplementary Figures S1-S181.</p>
Intervention effect of person profiles for people with advanced dementia: Stepped-wedge cluster randomised trial
<p>Dataset related to study <a href="https://kofam.ch/en/snctp-portal/searching-for-a-clinical-trial/study/53440">SNCTP000003941</a></p> <div><strong># Project title</strong></div> <div>Intervention effect of person profiles for people with advanced dementia: Stepped-Wedge Cluster Randomised Trial.</div> <div> </div> <div><strong># Brief project overview</strong></div> <div>This is Data coming out of the IPOS-Dem project, a Stepped-wedge Cluster Randomised Trial (SW-CRT). Please make shure to check out the SW-CRT protocol for more details at <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a>. </div> <div> </div> <div>We are currently working on our main study (SW-CRT) analysis. While we remain dedicated to open science, our priority is to ensure the utmost integrity of our ongoing research.</div> <div> </div> <div><strong># Contributors</strong></div> <div>Frank Spichiger, Andrea Koppitz, André Meichtry</div> <div> </div> <div><strong>## Repository overview</strong></div> <div>|-- readme.md</div> <div>|-- documentation</div> <div> |--readme.md</div> <div> |--Datadictionary.html</div> <div>|-- data</div> <div> |--readme.md</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_IPOS-Dem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_IPOS-Dem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.feather</div> <div> |--csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_IPOS-Dem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_IPOS-Dem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.csv</div> <div>|-- analysis</div> <div> |--readme.md</div> <div> |--240229_Analysis_Main.html</div> <div> |--240229_Analysis_Main.qmd</div> <div> |--grateful-refs.bib</div> <div> |--output</div> <div> |--models-REML.csv</div> <div> |--models.csv</div> <div> |--subscale_estimates.csv</div> <div> |--figures</div> <div> |--Datashowcasing-1.png</div> <div> |--DEBI-Predictions-Plot-1.png</div> <div> |--DPII-Plot-1.png</div> <div> |--Model_1-Plots-1.png</div> <div> |--Model_1-Plots-2.png</div> <div> |--Model_2-Plots-1.png</div> <div> |--Model_2-Plots-2.png</div> <div> |--Model_3-Plot-1.png</div> <div> |--Model_4-Plot-1.png</div> <div> |--Model_5-Plot-1.png</div> <div> </div> <div> </div> <div><strong># Applicable instructions</strong></div> <div>The R markdown files were generated using <a href="https://www.r-project.org">R 4.2.2</a> in <a href="https://posit.co/downloads/">RStudio 2023.12.1</a> for MacOS X you can run them using free software but will need to install R, Rstudio and the packages cited at the end of the quarto or the rendered html we used.</div> <div> </div> <div><strong># Additional resources</strong></div> <div>- Main study protocol: <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a></div> <div>- QUALIDEM measure used: <a href="https://doi.org/10.1186/1477-7525-11-91">https://doi.org/10.1186/1477-7525-11-91</a> </div> <div>- Pos-Pal consortium with more information on the measure: <a href="https://www.pos-pal.org">https://www.pos-pal.org</a></div> <div>- IPOS-Dem translation and adaption: <a href="https://www.doi.org/10.1186/s41687-022-00420-7">https://www.doi.org/10.1186/s41687-022-00420-7</a></div> <div>- IPOS-Dem inter-rating reliability: <a href="https://doi.org/10.1371/journal.pone.0286557">https://doi.org/10.1371/journal.pone.0286557</a> </div>
Nano-SMSI on Bimetallic FePt Clusters [doi: 10.1021/acs.jpcc.3c03896]
<p>Raw data, meta data and corresponding lists of figures are included. [Paper doi: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpcc.3c03896">10.1021/acs.jpcc.3c03896</a>]</p>
CDIPS Light Curves from the paper "Confirming the Tidal Tails of the Young Open Cluster Blanco 1 with TESS Rotation Periods"
<p>This dataset contains the CDIPS light curves used in the paper "Confirming the Tidal Tails of the Young Open Cluster Blanco 1 with TESS Rotation Periods". The abstract of the original paper is as follows.</p> <p>Blanco 1 is an ≈ 130 Myr open cluster located 240 pc from the Sun below the Galactic plane. Recent studies have reported the existence of diffuse tidal tails extending 50–60 pc from the cluster center, based on the positions and velocities measured by Gaia. To independently assess the reality and extent of this structure, we used light curves generated from TESS full-frame images to search for photometric rotation periods of stars in and around Blanco 1. We detected rotation periods down to a stellar effective temperature of ≈ 3100 K in 347 of the 603 cluster member candidates for which we have light curves. For cluster members in the core and candidate members in the tidal tails, both within a temperature range of 4400 to 6200 K, 74% and 72% of the rotation periods are consistent with the single-star gyrochronological sequence, respectively. In contrast, a comparison sample of field stars yielded gyrochrone-consistent rotation periods for only 8.5% of stars. The tidal tail candidates' overall conformance to the core members' gyrochrone sequence implies that their contamination ratio is consistent with zero and < 0.33 at the 2σ level. This result confirms the existence of Blanco 1 tidal tails and doubles the number of Blanco 1 members for which there are both spatio-kinematic and rotation-based cluster membership verification. Extending the strategy of using TESS light curves for gyrochronology to other nearby young open clusters and stellar associations may provide a viable strategy for mapping out their dissolution and broadening the search for young exoplanets.</p>
Orbit positions for the DS sources in the IRS 13 cluster close to Sgr A*
<p>This data is related to the manuscript entiteld "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure", published in the ApJ in 2024.</p> <p>We use the Keplerian elements and the data published in "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. I. Detection of a Rich Population of Dusty Objects in the IRS 13 Cluster" and "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure" to create the orbital solutions published in this data set.</p> <h2>Organization of the data:</h2> <h3>Text files</h3> <p>All .txt files are the output of the fitted Keplerian approximation of the related source. The first column indicates the date, the second and third one the RA and DEC position in arcseconds, respectively. Please note that these positions are projected on the sky with respect to Sgr A<em>. </em>Since all sources are located<em> "to the right" </em>and <em>"below"</em> Sgr A, the related positions contain a minus. </p> <h3>Image files</h3> <p>The related .png files show the plotted .txt files together with the data points published in the manuscript entitled "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. I. Detection of a Rich Population of Dusty Objects in the IRS 13 Cluster".</p> <h3>Statistics and Uncertainties</h3> <p>In addition, we publish the outcome of the Markow-Chain-Monte-Carlo (MCMC) simulations as .pdf files. These files indicate the uncertainty range and statistical robustness of the analysis presented in the manuscript entitled "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure". Due to the file number restrictions, we uploaded a .rar file that contains all pdf files.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets and scripts for the publication "Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe1-xO) at Elevated Temperatures: Accurate force field from Deep Learning"
<div> <div> <div> <div> <p><strong>All the datasets and scripts for the publication"Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe<sub>1-x</sub>O) at Elevated Temperatures: Accurate force field from Deep Learning".</strong></p> <p>This database contains high-fidelity datasets for non-stoichiometric wüstite (Fe₁₋ₓO), including atomic coordinates, energies, and forces generated through ab initio molecular dynamics (AIMD) and refined using Deep Potential (DP) training. The dataset encompasses bulk phases, vacancy structures, and surface orientations, enabling accurate modeling of defect clusters and thermodynamic properties. It supports machine-learning force field development, offering insights into defect formation and large-scale simulations of Fe₁₋ₓO systems at elevated temperatures.</p> </div> </div> </div> </div> <div> <p>Description of the File Structure of Fe1-xO_DeepMD_Code_Datasets_Analysis.zip:</p> <p>1. `<code>init</code>` Folder <br>This folder contains the foundational datasets and inputs used for training and developing the machine-learning force field for Fe₁₋ₓO. </p> <blockquote> <p>1.1 `<code>01.train_data</code>` Subfolder <br>This folder organizes data related to the initial training of the Deep Potential (DP) model. <br>- `<code>dpmd_dataset</code>`: Processed dataset ready for DeepMD training, containing atomic configurations, forces, and energies.<br>- `<code>dpmd_rawfiles</code>`: Raw files from ab initio molecular dynamics (AIMD) simulations, serving as the source for generating training datasets.</p> <p>1.2 `<code>02.develop_data</code>` Subfolder<br>Contains `<code>.vasp</code>` files representing structural data used to develop and refine the force field. The structures include bulk, vacancy, and surface configurations of Fe₁₋ₓO. <br>- Files labeled `<code>bulk</code>` represent bulk Fe₁₋ₓO systems with varying lattice constants. <br>- Files labeled `<code>defect</code>` represent Fe and O vacancy structures (single and double vacancies). <br>- Files labeled `<code>surface</code>` represent Fe₁₋ₓO surface structures in various crystallographic orientations. <br><br></p> </blockquote> <p>2. `<code>run</code>` Folder<br>This folder contains files and logs generated during iterative training and testing of the DP force field, as well as subfolders for each iteration of the training process. </p> <blockquote> <p>2.1 Iteration Folders (`<code>iter.000000</code>` to `<code>iter.000024</code>`):<br>Each folder represents an iteration in the iterative refinement of the DP model, with three subfolders: <br>- `<code>00.train</code>`: Contains training data and outputs for the DP model during the current iteration. <br>- `<code>01.model_devi</code>`: Tracks deviations between DP predictions and ab initio results, guiding dataset selection for the next iteration. <br>- `<code>02.fp</code>`: Stores first-principles (FP) results from CP2K used to improve DP model accuracy. </p> <p>2.2 Other Key Files: <br>- `<code>cp2k.input</code>`: Input file for CP2K, used for performing ab initio calculations on configurations during the iterative process. <br>- `<code>dpdispatcher.log</code>`: Log file tracking the progress of data dispatching and task execution. <br>- `<code>dpgen.log</code>`: Log file recording operations of DPGEN during dataset generation and force field development. <br>- `<code>dpgen_nohup.sh</code>`: Script for running DPGEN in the background. <br>- `<code>machine_slurm_cp2k.json</code>`: Configuration file specifying computing resources for CP2K simulations in a cluster environment. <br>- `<code>param_cp2k.json</code>`: Parameter file for CP2K calculations, defining simulation settings. <br>- `<code>record.dpgen</code>`: Record of iterative processes, including input parameters and outputs for each stage.</p> </blockquote> <p>This organized structure ensures a systematic approach to dataset preparation, model training, and iterative refinement for developing accurate machine-learning potentials for Fe₁₋ₓO.</p> <p>graph-compress.0330.pb is the final compressed DeepMD potential parameters.</p> </div>
Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas
<p>The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2017 and 2021</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as Shapefile.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p>
Reproducible analysis from SiRCle (Signature Regulatory Clustering)
<p>This contains the code and the data including the updates made during revisions for the manuscript: <strong><a href="https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1.abstract">SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer.</a> </strong></p> <p> </p> <p><strong>The data have been generated from CPTAC and TCGA.</strong> This includes no new data in this study.</p>
The Ones That Got Away: Chemical Tagging of Globular Cluster-Origin Stars with Gaia BP/RP Spectra
<p>Catalog of predictions associated with <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240900197K/abstract" target="_blank" rel="noopener">the paper "The Ones That Got Away: Chemical Tagging of Globular Cluster-Origin Stars with Gaia BP/RP Spectra."</a> The columns of the included tables are described in Appendix A.</p> <p>xp-validation_v1.fits is the predictions for the validation dataset (stars with known APOGEE abundances)</p> <p>xp-n-catalog_v1.fits is the catalog of new abundance predictions from the Gaia XP (BP/RP) spectra</p> <p>prediction-variances_v1.fits is the table of variances for each network output across 100 network predictions. The Gaia DR3 source ID is also included and corresponds to a source ID in the xp-n-catalog.</p>
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters
<p>In Tables 4 and 5, the <strong>Cluster</strong> column represents the name of the cluster. </p> <p><strong>Table 4</strong>: The columns <strong>ra</strong>, <strong>dec</strong>, <strong>pmra</strong>, <strong>pmdec</strong>, and <strong>parallax</strong> correspond to the median values for the cluster's position, parallax, and proper motions, respectively. The <strong>[Fe/H]</strong> and <strong>[Fe/H]_err</strong> columns indicate the cluster's [Fe/H] and its associated error. The <strong>logt</strong> and <strong>log_t_err</strong> columns represent the logarithmic age and its error, while the <strong>m-M</strong> and <strong>m-M_err</strong> columns denote the distance modulus and its error. Additionally, the <strong>E(BP-RP)</strong> and <strong>E(BP-RP)_err</strong> columns specify the cluster's reddening and its error, and the <strong>A_V</strong> and <strong>A_V_err</strong> columns represent the extinction and its error.</p> <p><strong>Table 5</strong>: The <strong>rc_pc</strong> and <strong>e_rc_pc</strong> columns indicate the core radius and its error, while the <strong>rt_pc</strong> and <strong>e_rt_pc</strong> columns represent the tidal radius and its error. The <strong>rh_pc</strong> column provides the radius containing half of the total number of stars in the cluster, and the <strong>rhm_pc</strong> column gives the half-mass radius. The <strong>R_J</strong> and <strong>R_J_err</strong> columns represent the Jacobi radius and its error. The <strong>mass</strong> and <strong>mass_err</strong> columns show the total mass of the cluster and its error, and the <strong>fb</strong> column denotes the binary fraction of the cluster. Finally, the <strong>trlx</strong> and <strong>trlx_err</strong> columns represent the relaxation time and its error. The units of<strong> trlx</strong> and <strong>trlx_err</strong> are Myr</p> <p>Note: NULL values for <strong>rc_pc, e_rc_pc, rt_pc, </strong>and <strong>e_rt_pc </strong>indicate the inapplicability of the RDP method. For <strong>Bootes I, NGC 104, NGC 3201, NGC 6121, NGC 6544, </strong>and <strong>NGC 6656</strong>, the parameters listed as “N/A”—including <strong>rhm, rJ, rJ_err, mass, mass_err, fb, trlx_Myr,</strong> and<strong> trlx_err</strong>—cannot be determined using our methods due to their faint magnitudes. This limitation arises because Gaia’s observational capacity extends only to 21 mag.</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.