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.
2,556
datasets available to search
ShareScore release 0.9.0
Dataset results
2,556 results for “catalogs”
Figure 4 from: Wojnarski M, Hanken Kurtz D (2016) Paperity Central: An Open Catalog of All Scholarly Literature. Research Ideas and Outcomes 2: e8462. https://doi.org/10.3897/rio.2.e8462
Figure 4 - An introductory video presenting our Open Science Prize project.
The data for a catalog of newly discovered close binary open clusters in the Milky Way from Gaia DR3
<p>The database for a catalog of newly discovered close binary open clusters in the Milky Way from Gaia DR3.</p> <p>It corresponds to the study of Zhongmu Li, Zhanpeng Zhu in 2024, which was submitted to AJ. When one use these data, please cite to that work. </p> <p>The database contains four parts, i.e., a parameter file and four directories. They are explained as follows.</p> <p>The file of ''data_tableI.csv'' gives the spatial positions and parallaxs of 13 newly discovered close binary open clusters (CBOCs).</p> <p>There are 11 columns in this file. The colums are for Name1, RA1, DEC1, Parallax1, Rtidal1, Name2, RA2, DEC2, Parallax2, Rtidal2 respectively. </p> <p>They correspond to the contents of a manuscript that was submitted to AJ.</p> <p>The file of ''data_tableII.csv'' contains the data of the fundamental parameters of 13 newly discovered CBOCs, together with their astrometric information. PBOC means primordial binary open cluster member. Distance modulus, color excess, age, and metal abundance of the clusters were obtained by fitting Parsec isochrones and Powerful CMD code. Proper motion data for the clusters were provided by HR23.</p> <p>Columns 3-14 show the basic parameters (distance modulus, color excess, age, and metal abundance) and errors of the cluster. Columns 15-16 are the proper motion data for the clusters. Column 17 is the number of member stars of the cluster.</p> <p>The file of ''data_tableIII.csv'' contains the data of Parameters for checking gravitationally bound and unbound cluster pairs. The subscripts 1 and 2 indicate the first and second sub-clusters respectively. Rroche is the Roche radius. RV 1, RV 2 and ΔRV denote the radial velocities of two member clusters and their difference. Porb and Vorb denote orbital period and orbital velocity.</p> <p>There are 13 columns in this file. The colums are for Name1, Name2, d12, r1, r2, Rroche, Mass1, Mass2, Porb, RV1, RV2, ΔRV, Vorb respectively. </p> <p>The file of ''memeber_allp.txt'' gives the list of member stars of 13 pairs of clusters. Data provided by HR23.</p> <p>If you have any problems with using these data, send an email to zhongmuli@126.com.</p>
Figs. 11–14. 11. Dolium melanostomum Jay, 1839 in Catalog Of Recent Type Specimens In The Division Of Invertebrate Zoology, American Museum Of Natural History. V. Mollusca, Part 2 (Class Gastropoda [Exclusive Opisthobranchia And Pulmonata With Supplements To Gastropoda [Opisthobranchia], And Bivalvia
Figs. 11–14. 11. Dolium melanostomum Jay, 1839 (holotype—AMNH 56115), X 0.45. 12.
Galactic Component Classification For the all-sky PLATO Input Catalog
<div> <h1>PLATO Targets Classification</h1> <p>This repository contains the full list of PLATO targets, classified into their Galactic Component membership. The basis for the catalogue is the all-sky PLATO input catalog (asPIC) by Montalto2021, crossmatched with Gaia DR3.</p> <h2>Files</h2> <p>There are four files included in this repository:</p> <ol> <li> <p><strong>targets_classified</strong>:</p> <ul> <li>Description: The entire sample, classified into thin disk, thick disk candidate, thick disk, halo candidate, and halo.</li> <li>Note : Classification is only performed if relative uncertainties no greater than 20% in the following columns: "ra", "dec", "pmra", "pmdec", "parallax", and "radial_velocity". For targets where this requirement is not fulfilled, the related columns are filled with NaN.</li> <li>Format: 2,675,538 rows, 61 columns</li> </ul> </li> <li> <p><strong>LOPS2</strong>:</p> <ul> <li>Description: A subselection of targets_classified in the LOPS2 field.</li> <li>Galactic Coordinates: l=255.9375°, b=-24.62432°</li> <li>Additional Column: "n_cameras" specifying the number of PLATO cameras that will observe the target.</li> <li>Format: 169,438 rows, 62 columns</li> </ul> </li> <li> <p><strong>LOPN1</strong>:</p> <ul> <li>Description: A subselection of targets_classified in the LOPN1 field.</li> <li>Galactic Coordinates: l=81.56250°, b=24.62432°</li> <li>Format: 173,735 rows, 62 columns</li> </ul> </li> <li> <p><strong>special_target_list</strong>:</p> <ul> <li>Description: A selection of 47 targets, kinematically classified as halo stars, within the high-SNR P1 PLATO sample.</li> <li>Additional Column: "n_cameras" specifying the number of PLATO cameras that will observe the target, and "Field" specifying if target belongs to LOPS2 or LOPN1 field</li> <li>Format: 47 rows, 63 columns</li> </ul> </li> </ol> <h2>Classification Criteria</h2> <p>We classify only those stars into Galactic components that have relatively certain kinematic parameters. Specifically, we require no more than 20% relative uncertainty in the following columns: "ra", "dec", "pmra", "pmdec", "parallax", "radial_velocity". This results in 2,283,538 out of the total 2,675,539 stars having a component classification.</p> <h2>Analysis Conditions</h2> <p>For the analysis in our paper, we applied the following additional conditions:</p> <ul> <li>Population.notnull()</li> <li>Stellar type = FGK</li> <li>Radius > 0</li> <li>Mass > 0</li> <li>Teff > 0</li> <li>Logg.notnull()</li> <li>[Fe/H].notnull()</li> </ul> <p>Applying these conditions results in 1,830,288 entries remaining.</p> <h2>Columns</h2> <p>The columns in the dataset are as follows:</p> <table> <tbody> <tr> <th>Column Name</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>gaiaID_DR2</td> <td>Gaia DR2 ID (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>gaiaID_DR3</td> <td>Gaia DR3 ID (matched to DR2 catalog, based on angular distance)</td> </tr> <tr> <td>parallax</td> <td>Parallax of target (mas), from Gaia DR3</td> </tr> <tr> <td>e_parallax</td> <td>Error on parallax</td> </tr> <tr> <td>ra</td> <td>Right Ascension (deg) from Gaia DR3</td> </tr> <tr> <td>e_ra</td> <td>Error on Right Ascension</td> </tr> <tr> <td>dec</td> <td>Declination (deg) from Gaia DR3</td> </tr> <tr> <td>e_dec</td> <td>Error on declination</td> </tr> <tr> <td>pmra</td> <td>Proper motion in right ascension direction (mas/yr), from Gaia DR3</td> </tr> <tr> <td>e_pmra</td> <td>Error on proper motion in right ascension direction</td> </tr> <tr> <td>pmdec</td> <td>Proper motion in declination direction (mas/yr), from Gaia DR3</td> </tr> <tr> <td>e_pmdec</td> <td>Error on proper motion in declination direction</td> </tr> <tr> <td>radial_velocity</td> <td>Radial velocity (km/s), from Gaia DR3</td> </tr> <tr> <td>e_radial_velocity</td> <td>Radial velocity error</td> </tr> <tr> <td>[alpha/Fe]</td> <td>Alpha elemental abundance from Gaia RV spectra, alphafe_gspspec in Gaia DR3</td> </tr> <tr> <td>e_[alpha/Fe]_lower</td> <td>Lower error on alpha element abundance</td> </tr> <tr> <td>e_[alpha/Fe]_upper</td> <td>Upper error on alpha element abundance</td> </tr> <tr> <td>[Fe/H]</td> <td>Metallicity estimate, either from RVS spectra (mh_gsspec in Gaia DR3, with first 13 quality flags equal to 0, Recio-Blanco2023) or XGBOOST (Andrae2023)</td> </tr> <tr> <td>[Fe/H]_source</td> <td>Source of the metallicity estimate</td> </tr> <tr> <td>e_[Fe/H]_lower</td> <td>Lower error on the metallicity estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>e_[Fe/H]_upper</td> <td>Upper error on the metallicity estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>logg</td> <td>log g estimate, either from RVS spectra (logg_gsspec in Gaia DR3, with first 13 quality flags equal to 0, Recio-Blanco2023) or XGBOOST (Andrae2023)</td> </tr> <tr> <td>logg_source</td> <td>Source of the log g estimate</td> </tr> <tr> <td>e_logg_lower</td> <td>Lower error on the log g estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>e_logg_upper</td> <td>Upper error on the log g estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>[Fe/H]_apogee</td> <td>Metallicity estimate from APOGEE-DR17</td> </tr> <tr> <td>e_[Fe/H]_apogee</td> <td>Error on APOGEE metallicity estimate</td> </tr> <tr> <td>[alpha/M]_apogee</td> <td>Alpha abundance estimate from APOGEE-DR17</td> </tr> <tr> <td>e_[alpha/M]_apogee</td> <td>Error on APOGEE alpha estimate</td> </tr> <tr> <td>logg_apogee</td> <td>log g estimate from APOGEE-DR17</td> </tr> <tr> <td>e_logg_apogee</td> <td>Error on APOGEE log g estimate</td> </tr> <tr> <td>[Fe/H]_galah</td> <td>Metallicity estimate from GALAH DR3</td> </tr> <tr> <td>e_[Fe/H]_galah</td> <td>Error on GALAH metallicity</td> </tr> <tr> <td>[alpha/Fe]_galah</td> <td>Alpha abundance estimate from GALAH DR3</td> </tr> <tr> <td>e_[alpha/Fe]_galah</td> <td>Error on GALAH alpha abundance</td> </tr> <tr> <td>logg_galah</td> <td>log g estimate from GALAH DR3</td> </tr> <tr> <td>e_logg_galah</td> <td>Error on GALAH log g</td> </tr> <tr> <td>GLON</td> <td>Galactic longitude (deg)</td> </tr> <tr> <td>GLAT</td> <td>Galactic latitude (deg)</td> </tr> <tr> <td>U</td> <td>Heliocentric velocity in the direction of the Galactic center (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>V</td> <td>Heliocentric velocity in the direction of the Galactic rotation (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>W</td> <td>Heliocentric velocity in the direction of the North Galactic Pole (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>UW</td> <td>Total non-circular velocity UW = sqrt(U^2 + W^2) (km/s), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>R</td> <td>Distance from the Galactic center in the Galactic plane (kpc), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>Z</td> <td>Distance from the Galactic plane (kpc), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>gaiaV</td> <td>V-band magnitude (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_gaiaV</td> <td>Error on V-band magnitude</td> </tr> <tr> <td>Gmag</td> <td>Gaia G magnitude (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Gmag</td> <td>Error on G magnitude</td> </tr> <tr> <td>Radius</td> <td>Radius of star (solar radii) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Radius</td> <td>Error on radius</td> </tr> <tr> <td>Mass</td> <td>Mass of star (solar masses) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Mass</td> <td>Error on mass</td> </tr> <tr> <td>Teff</td> <td>Effective temperature of star (K) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Teff</td> <td>Error on effective temperature</td> </tr> <tr> <td>Stellar Type</td> <td>Classification into M or FGK type, based on asPIC catalog (Montalto2021)</td> </tr> <tr> <td>u1</td> <td>The first quadratic limb-darkening parameter, based on grid by Morello2022</td> </tr> <tr> <td>u2</td> <td>The second quadratic limb-darkening parameter, based on grid by Morello2022</td> </tr> <tr> <td>TD/D</td> <td>Probability ratio thick disk/thin disk (see Section 2.2. in our paper), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>TD/H</td> <td>Probability ratio thick disk/halo (see Section 2.2 in our paper), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>Population</td> <td>Galactic component classification (Thin Disk, Thick Disk, Halo, Thick Disk Candidate, Halo Candidate), see paper for definitions, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>n_cameras</td> <td>Number of PLATO cameras observing the target (Additional Column for LOPS2, LOPN1, and Special Target List)</td> </tr> <tr> <td>field</td> <td>Flag for LOPS2 or LOPN1 fields (Additional Column for Special Target List)</td> </tr> </tbody> </table> </div>
Plate 1 from: Chamorro W, Marin-Armijos D, Asenjo A, Vaz-De-Mello FZ (2019) Scarabaeinae dung beetles from Ecuador: a catalog, nomenclatural acts, and distribution records. ZooKeys 826: 1-343. https://doi.org/10.3897/zookeys.826.26488
Plate 1 - Natural ecosystems in Ecuador (Modified from Sierra 1999).
Relocated Earthquake Catalog for Eastern Indonesia (April 2009 to November 2018)
<p>Relocated Earthquake Catalog for Eastern Indonesia (April 2009 to November 2018). </p> <p>Please refer to:</p> <p>Supendi, P., Nugraha, A.D., Widiyantoro, S., Pesicek, J.D., Thurber, C.H., Abdullah, C.I., Daryono, D., Wiyono, S.H., Shiddiqi, H.A., and Rosalia, S. (2020). Relocated aftershocks and background seismicity in Eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences, Geophysical Journal International, ggaa118, https://doi.org/10.1093/gji/ggaa118</p> <p> </p>
Figure 5 in TaXonomic Catalog of the Brazilian Fauna: Hydraenidae (Insecta: Coleoptera), diversity and distribution
Figure 5. Number of Hydraenidae species recorded for each state of Brazil by genus.
earthquake event waveforms, phase picks and catalogs in this study
<p>The uploaded repository contains the following files:</p> <ul> <li><strong>25catlog_CENC.txt</strong>: The catalog of 25 earthquakes published by the China Earthquake Networks Center (CENC) in the study area during the observation period.</li> <li><strong>25catlog_this_study.txt</strong>: The same 25 events as published by CENC, reprocessed for this study to verify the accuracy of the magnitudes obtained.</li> <li><strong>hypoDD.pha</strong>: The phase file of seismic events generated in this study.</li> <li><strong>relocated_catalog_by_hypoDD_this_study_UTC+8.txt</strong>: The final earthquake catalog relocated using hypoDD.</li> <li><strong>xindianzi & luoguanshan swarm event_waveform_data.rar</strong>: Waveform data for seismic events within the Xindianzi and Luoguanshan swarms.</li> <li><strong>xindianzi & luoguanshan swarm event_waveform_figures.rar</strong>: Figures of waveform data from the two swarms.</li> <li><strong>event_waveforms.rar</strong>: Waveform data for all seismic events identified in this study.</li> </ul>
ML-Enhanced catalog of the 5 April 2024 Tewksbury aftershocks
<p>Machine-learning-enhanced catalog of the aftershocks of the 5 April 2024 M4.8 Tewksbury, New Jersey earthquake. It accompanies the following article submitted to Geophysical Research Letters:</p> <p>The 5 April 2024 Tewksbury, New Jersey aftershock sequence resolved with machine-learning-enhanced detection methods</p> <p>This repository contains the pre-relocation catalog (<span><a href="https://zenodo.org/api/records/14058325/draft/files/catalog_Tewksbury_aftershocks_before_relocation.csv/content" target="_blank" rel="noopener noreferrer">catalog_Tewksbury_aftershocks_before_relocation.csv</a></span>) and the relocated catalog (<a href="https://zenodo.org/api/records/14058325/draft/files/catalog_Tewksbury_aftershocks_relocated.csv/content" target="_blank" rel="noopener noreferrer">catalog_Tewksbury_aftershocks_relocated.csv</a>).</p> <p> </p> <p>Columns of the pre-relocation catalog:</p> <ul> <li>event_id: Unique string identifying an event.</li> <li>Mw: Moment magnitude, computed when possible only (so, most of the events have NaNs because they were too small to estimate Mw).</li> <li>Mw_err: Error on moment magnitude.</li> <li>Mw*: Approximate moment magnitude.</li> <li>longitude: Event longitude.</li> <li>latitude: Event latitude.</li> <li>depth: Event depth in km.</li> <li>origin_time: Event UTC origin time.</li> <li>origin_time_local: Event local origin time.</li> <li>hmax_unc: Event location maximum horizontal 1-std uncertainty, in km.</li> <li>hmin_unc: Event location minimum horizontal 1-std uncertainty, in km.</li> <li>vmax_unc: Event location maximum horizontal 1-std uncertainty, in km.</li> <li>az_hmax_unc: Azimuth of the event location maximum horizontal uncertainty.</li> <li>tt_rms: Residual travel-time, in sec.</li> <li>n_dev: Network-averaged correlation coefficient in units of CC RMS (matched-filtering).</li> <li>potential_quarry_blast: True or False. Flags potential quarry blast, ie, anthropogenic earthquake.</li> </ul> <p>Columns of the relocated catalog:</p> <ul> <li>event_id: Unique string identifying an event.</li> <li>origin_time: Event UTC origin time.</li> <li>longitude: Event longitude.</li> <li>latitude: Event latitude.</li> <li>depth: Event depth in km.</li> <li>Mw*: Approximate moment magnitude.</li> <li>hmax_unc: Event location maximum horizontal uncertainty, 95% CI, in km. Computed with bootstrapping.</li> <li>hmin_unc: Event location minimum horizontal uncertainty, 95% CI, in km. Computed with bootstrapping.</li> <li>vmax_unc: Event location maximum horizontal uncertainty, 95% CI, in km. Computed with bootstrapping.</li> <li>az_hmax_unc: Azimuth of the event location maximum horizontal uncertainty.</li> <li>quality: "A" or "B". "A" events were relocated with stronger inter-event linkage in HypoDD.</li> </ul>
An adaptable Random Forest model for the declustering of earthquake catalogs
<p>Random Forest models trained on different proportion of the synthetic earthquake catalog data set.</p> <p>The name of the model is defined as RF_XP_Y_Z.sav where:</p> <p> - X is the number of neighbor considered in the training stage;</p> <p> - Y is the number of feature considered in the training stage;</p> <p> - Z is the percentage (0-100) of the total data set used for the training stage. </p> <p>More details on how to use the model in <a href="https://github.com/florentaden/mldeclustering">this Github page</a>.</p>
Anadromous cataloging and fish inventory in select drainages of the Upper Tanana and Yukon Rivers 2019
<p>During the summer of 2019, Alaska Department of Fish and Game Division of Sport Fish staff conducted a rapid systematic inventory of anadromous fish distribution and associated aquatic and riparian habitat in select drainages of the upper Yukon River and Tanana River. Target streams will be selected to fill gaps in coverage of the State of Alaska's <i>Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes</i> (AWC) for freshwater habitats expected to support anadromous fish populations likely to be impacted by human activities. Two crews sampled standardized target stream reaches using electrofishers with sufficient effort to collect all species (perhaps with the exception of rare species) of the extant fish community. At each sampling site, crews documented standard aquatic and riparian habitat characteristics. For each water body in which anadromous fish are observed, nominations to the AWC were submitted. One hundred sites were surveyed during this study period, a slightly smaller number than what was expected due to long term drying patterns in the region that led to farther travel times in addition to flooding events that happened after heavy rains.</p>
Figure 13 from: Marek PE, Krejca JK, Shear WA (2016) A new species of Illacme Cook & Loomis, 1928 from Sequoia National Park, California, with a world catalog of the Siphonorhinidae (Diplopoda, Siphonophorida). ZooKeys 626: 1-43. https://doi.org/10.3897/zookeys.626.9681
Figure 13 - Illacme tobini sp. n.: ♂ holotype. Scale bar 1 mm. (Catalog #: MPE00735.)
Figure 7 from: Moore MR, Jameson ML, Garner BH, Audibert C, Smith ABT, Seidel M (2017) Synopsis of the pelidnotine scarabs (Coleoptera: Scarabaeidae: Rutelinae: Rutelini) and annotated catalog of the species and subspecies. ZooKeys 666: 1-349. https://doi.org/10.3897/zookeys.666.9191
Figure 7 - Chipita mexicana (Ohaus) female specimen from FSCA. A Dorsal habitus B Lateral habitus.
Figure 52 from: Moore MR, Jameson ML, Garner BH, Audibert C, Smith ABT, Seidel M (2017) Synopsis of the pelidnotine scarabs (Coleoptera: Scarabaeidae: Rutelinae: Rutelini) and annotated catalog of the species and subspecies. ZooKeys 666: 1-349. https://doi.org/10.3897/zookeys.666.9191
Figure 52 - Parhoplognathus limbatipennis (Ohaus) from USNM. A Dorsal habitus B Lateral habitus.
The integrated properties of the molecular clouds from the JCMT CO(3-2) High Resolution Survey - Cloud integrated property catalog
<p>Catalog of integrated properties of the molecular clouds identified within the JCMT CO(3-2) High Resolution Survey using the SCIMES software (https://github.com/Astroua/SCIMES). The catalog is provided as ASCII (.txt) and astropy.table (.fits) tables. Detailed description of the catalog content can be found in the Table 1 of the associated paper (https://arxiv.org/abs/1812.04688).</p>
Figure Sets, Catalog Tables, and Other Technical Materials Related to "ALMA-IMF IX: Catalog and Physical Properties of 315 SiO Outflow Candidates in 15 Massive Protoclusters"
<p>This Zenodo DOI contains additional data products and technical files for the publication "ALMA-IMF IX: Catalog and Physical Properties of 315 SiO Outflow Candidates in 15 Massive Protoclusters" (Towner et al. 2024).</p> <p>All files, metadata, and other components and materials in this Zenodo entry are licensed under Creative Commons license CC-BY-NC 4.0. This license allows for non-commercial use so long as proper attribution is given to the original creator. Commercial use of these materials is prohibited. Further details of this license can be found on the Creative Commons website at: https://creativecommons.org/licenses/by-nc/4.0/ </p> <p> </p> <p>This upload contains:</p> <p>1. The Figure Set for Figure 1. The .png summary figures are contained in 5 zip files (Figset1_part1of5.zip, etc.). The file naming structure is <fieldname>_<candididatename>.summary.png, i.e. the summary figure for Candidate #1 in Field G008.67 is G008.67_s1.summary.png.</p> <p>2. The Figure Set for Figure C1. All 15 .png files are contained in the file "FigsetD1_all.zip." Please note: due to post-acceptance edits at the proofing stage, there is no Appendix D in the ApJ article. All files with "D1" in the name refer to either Figure Set C1 or Table C1 in the ApJ publication, as appropriate. </p> <p>3. ECSV versions of Table 3 and Table C1 (labeled as "D1" in this upload), and a LaTeX version of Table 3.</p> <p>4. CRTF apertures and position-velocity paths for all candidates, and FITS files of the position-velocity diagrams for all candidates. The relevant file is "apertures_paths_pvdiagrams.zip." This file contains a directory structure. There are 15 subdirectories, each named for the field to which it corresponds. Within each subdirectory are the aperture and pvpath CRTF files, and the PV diagram FITS files. </p> <p> a. Apertures follow the naming convention s??_aperture.crtf, where "??" is the candidate number. Apertures are color-coded by outflow color (red outflows use red lines, blue outflows use blue, and bipolar outflows use purple/magenta). The aperture for G008.67 Candidate #1 is named s1_aperture.crtf, etc.</p> <p> b. Position-velocity paths follow the naming convention "s??_path<.direction>.crtf" where <.direction> is ".ra" or ".dec" or blank (""). Directions are necessary because CARTA saves point-like regions in the order in which they were last modified. In order to ensure the points are used in the correct order in pvextractor (or another program), users should first sort the points in the CRTF file in order of increasing ra (for files containing ".ra") or increasing dec (for files containing ".dec"). Files without a <.direction> in the name indicate that the candidate is classified as "complex or cluster" and so sorting is not particularly relevant. </p> <p> c. Position-velocity diagrams are uploaded as FITS files, and follow the naming convention "s??_pvslice.fits" where "??" is the candidate number. Note that some paths are extremely short and thus their PV diagrams can be difficult to read. Although all PV diagrams are shown as square in Towner et al. (2024), allowing the PV diagram to display with its natural aspect ratio may be more useful in some cases.</p> <p> </p> <p>Final note:</p> <p>The FITS cubes used in this analysis are not uploaded to Zenodo due to upload size limitations. The full cubes and continuum images for each field can be found on the ALMA-IMF Project website (https://www.almaimf.com/data.html). Cubes cut to +/- 95 km/s, noise cubes, and cubes in units of K instead of Jy/beam can be requested by emailing Dr. Allison Towner at apmtowner [at] gmail.com.</p>
Anadromous cataloging and fish inventory in select drainages of the Upper Tanana and Yukon Rivers 2019
Open the record for dataset details and reuse information.
Southern Cascadia earthquake catalog 2014-July to 2015-October
Open the record for dataset details and reuse information.
Generation of a transcriptome catalog for global analysis of gene expression in Ampelomyces quisqualis during mycoparasitization
GEO Series GSE22888. Ampelomyces quisqualis. 11 samples. Type: Expression profiling by array.
Atlas of pathogen-sensitive myeloid lncRNA networks in humans (SMyLR catalog) [ChIRP-seq]
GEO Series GSE268546. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.