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417 results for “IA”
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
IA Tweets Analysis Dataset (Spanish)
<h3><strong>Cite as</strong></h3> <p><em><strong>Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.</strong></em></p> <h3>General Description</h3> <p>This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (<a href="https://doi.org/10.1109/IE61493.2024.10599899" target="_blank" rel="noopener">10.1109/IE61493.2024.10599899</a>) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.</p> <h3>Data Collection Method</h3> <p>Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.</p> <h3>Dataset Content</h3> <ul> <li><strong>ID</strong>: A unique identifier for each tweet.</li> <li><strong>text</strong>: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.</li> <li><strong>polarity</strong>: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).</li> <li><strong>favorite_count</strong>: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.</li> <li><strong>retweet_count</strong>: The number of times this tweet has been retweeted. It is a non-negative integer.</li> <li><strong>user_verified</strong>: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.</li> <li><strong>user_default_profile</strong>: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.</li> <li><strong>user_has_extended_profile</strong>: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.</li> <li><strong>user_followers_count</strong>: The current number of followers the account has. It is a non-negative integer.</li> <li><strong>user_friends_count</strong>: The number of users that the account is following. It is a non-negative integer.</li> <li><strong>user_favourites_count</strong>: The number of tweets this user has liked since the account was created. It is a non-negative integer.</li> <li><strong>user_statuses_count</strong>: The number of tweets (including retweets) posted by the user. It is a non-negative integer.</li> <li><strong>user_protected</strong>: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.</li> <li><strong>user_is_translator</strong>: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.</li> </ul> <h3>Potential Use Cases</h3> <p>This dataset is aimed at academic researchers and practitioners with interests in:</p> <ul> <li>Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.</li> <li>Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.</li> <li>Exploring correlations between user engagement metrics and sentiment in discussions about AI.</li> </ul> <h3>Data Format and File Type</h3> <p>The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.</p> <h3>License</h3> <p>The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.</p>
IAS-Lab Collaborative Draping HAR dataset
<h2>Description</h2> <p>This dataset contains movement data for several subjects performing actions related to human-robot collaboration in an industrial carbon fiber draping process, such as draping and collaborative transport of carbon fiber plies. The collected dataset has been used to train and evaluate skeleton-based Human Action Recognition (HAR) models developed to provide a simple and intuitive way for the human operator to interact with the robot, such as signaling start and stop of the process or requesting robot’s assistance with specific tasks (e.g., inspection of specific parts).</p> <h2><br>Actions of interest</h2> <p>The dataset includes gestures designed to provide a simple and intuitive way for the operator to interact with the robot (e.g., “OK/NEXT” and “POINT” actions), short duration actions related to the beginning and end of collaborative transport operations (e.g., “PICK” and “PLACE” actions), and long duration actions related to the draping process (e.g., “TRANSPORT” and “DRAPING” actions) or general movements of the operator (e.g., “REST” and “WALK”). <br>The dataset also includes an additional unknown class (“UNKWN”) which represents various random movements that the operator might make during the collaborative process but that do not correspond to any of the actions of interest.</p> <table> <tbody> <tr> <td><strong>Action ID</strong></td> <td><strong>Action Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>A001</td> <td>OK/NEXT</td> <td>Raise one arm to signal the robot to continue the draping process</td> </tr> <tr> <td>A002</td> <td>POINT</td> <td>Point at a desired location with a straight right arm to trigger inspection</td> </tr> <tr> <td>A003</td> <td>PICK</td> <td>Raise the ply to trigger the collaborative transport</td> </tr> <tr> <td>A004</td> <td>PLACE</td> <td>Place the ply on the mold</td> </tr> <tr> <td>A005</td> <td>TRANSPORT</td> <td>Collaborative transportation</td> </tr> <tr> <td>A006</td> <td>DRAPE</td> <td>Manual draping of a ply</td> </tr> <tr> <td>A007</td> <td>REST</td> <td>Resting position, mainly waiting for the robot to complete its task</td> </tr> <tr> <td>A008</td> <td>WALK</td> <td>Walking across the workcell</td> </tr> <tr> <td>A009</td> <td>UNKWN</td> <td>Operator movements not related to the draping process</td> </tr> </tbody> </table> <h2> </h2> <h2>Dataset</h2> <p>Data has been collected from 7 participants, 2 female and 5 male, average age 27 (SD=3.0). Each participant performed 6 repetitions of each of the 8 actions considered for a collaborative draping process, and 18 repetitions of random movements for the unknow class. This results in a collected dataset containing 462 trimmed samples, where each sample is a sequence of 3D skeletons lasting approximately 3 seconds, containing only one action being performed. For all samples, 3D skeletons were obtained by means of the camera network installed in the laboratory, providing: (i) 3D skeletons from each camera in the network and (ii) 3D skeletons obtained by fusing the detections from each camera with a tracking algorithm; the output of the tracking algorithm provides a 3D skeleton representation robust to occlusions. All the 3D skeletons acquired are composed of 15 joints, with 3D coordinates expressed with respect to the camera network reference frame.</p> <p>Skeleton data for all sequences are provided in the `skeleton_data` folder. The dataset consists of a folder for each action of interest, with a subfolder for each participant and an individual text file for each action repetition performed by the participant. The naming convention for these files follows a pattern of the type “AxxxPyyyRzzzCwww.skeleton”, where "Axxx" represents the action id, "Pyyy" represents the id assigned to the participant, "Rzzz" represents the repeat number, and "Cwww" represents the id of the camera from which the 3D skeleton is derived; a network of 4 cameras was used to acquire the data, so “C001” denotes the first camera, “C002” the second, and so on, while “C000” represents the 3D skeletons obtained by merging all views.<br>Each of these files is provided as a “.skeleton” text files, similar to popular human action recognition dataset (e.g., NTU RGB+D and NTU RGB+D 120 action recognition datasets). In particular, each file includes a sequence of 3D skeletons with 25 joints following the OpenPose convention for joint numbering, but only the first 15 joints contain valid values since face keypoints were not considered during the acquisitions.</p> <p>Example python code to read/write and visualize the skeleton data is also provided in the `scripts` folder:<br>```python<br>python3 plot_sequence.py --data_dir ../skeleton_data<br>```</p> <p> </p> <h2>References</h2> <ol> <li>Allegro, D., Terreran, M., & Ghidoni, S. (2023). METRIC—Multi-Eye to Robot Indoor Calibration Dataset. Information, 14(6), 314. https://doi.org/10.3390/info14060314</li> <li>Terreran, M., Barcellona, L., & Ghidoni, S. (2023). A general skeleton-based action and gesture recognition framework for human–robot collaboration. Robotics and Autonomous Systems, 170, 104523. https://doi.org/10.1016/j.robot.2023.104523</li> <li>Terreran, M., Lazzaretto, M., & Ghidoni, S. (2022, June). Skeleton-based action and gesture recognition for human-robot collaboration. In International Conference on Intelligent Autonomous Systems (pp. 29-45). Cham: Springer Nature Switzerland.</li> <li>Carraro, M., Munaro, M., Burke, J., & Menegatti, E. (2019). Real-time marker-less multi-person 3D pose estimation in RGB-depth camera networks. In Intelligent Autonomous Systems 15: Proceedings of the 15th International Conference IAS-15 (pp. 534-545). Springer International Publishing.</li> </ol>
Analysis of the content of the H2020 project websites related to LEAs and IA.
<p>This is the dataset used in the article entitled "The disconnect between the goals of trustworthy AI for law enforcement and the EU research agenda". You can find more information about the results obtained, as well as the methodology used in the paper.</p>
ESA SEOM-IAS – Measurement and ACS database O3 UV region
<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for ozone according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in 2 zip files, one for the measurements (O3_UV_region_measurement_database_13112018.zip), one for the absorption cross sections (O3_UV_region_absorption_cross_section_database_13112018.zip) and a readme file (ESA_SEOM_IAS_spectra_O3UVRegion_readme.docx).</p> <p>The file “ACS_pTpoly_2nd+1st_order_BGcoefffit_constant_offset_V2.asc” in version II replaces the older version which contained an error in the wavelength axis. The readme file was updated, too.</p>
ESA SEOM-IAS – Measurement and line parameter database O3 MIR region
<p>The database contains measurements and line parameters generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded and analysed at the German Aerospace Center (DLR) and the University of Reims (URCA) to provide a new line position and intensity database for ozone fundamentals in the mid infrared region. The data are compiled in five zip files, three for the measurements (O3_MIR_region_DLR_measurement_database_20112018.zip, O3_MIR_region_DLR_measurement_N2O2broad_database_07012021.zip, O3_MIR_region_URCA_measurement_database_20112018.zip), two for the line parameter databases (O3_MIR_region_DLR_parameter_database_08012021.zip, O3_MIR_region_URCA_parameter_database_20112018.zip) and a readme file (ESA_SEOM_IAS_O3MIRRegion_readme_V3.docx).</p> <p>Note: The DLR parameter database is replaced by a newer version.</p>
Supplemental Movie 4: Subtypes of ICC-IM with different Ca2+ firing patterns in the IAS.
<p><strong><span>Supplemental Movie 4:<span> </span>S</span>ubtypes of ICC-IM with different Ca<sup>2+</sup> firing patterns in the IAS</strong></p> <p><span>Video from the distal edge of the internal anal sphincter (IAS) from a mouse expressing GCaMP6f exclusively in ICC using a 20x objective. Active ICC-IM show 2 patterns of Ca<sup>2+</sup> transients.<span> </span>Type I cells (* and green text) displayed stochastic Ca<sup>2+</sup> transients with short distances of spatial spread.<span> </span>Type II cells (* and yellow text) showed whole-cell flashes of activity. The still image and spatio-temporal (ST) maps (derived from the highlighted cells) and Ca<sup>2+</sup> traces shown in Fig. 20A-E were generated from this recording.<span> </span>Data correspond to figure in reference </span><span><span>(136)</span></span><span>.<span> </span></span></p>
Referencias incluidas en la revisión sistemática sobre IA y escritura académica
<p>Documento en formato RIS para importar y consultar las referencias seleccionadas en la revisión sistemática sobre inteligencia artificial y escritura académica (hasta 27/3/2024) en las bases de datos Scopus y Web of Knowledge.</p>
dataset for "Non-LTE Synthetic Observables of a Multidimensional Model of Type Ia Supernovae"
<p>Included here are datafiles supplementary to the article "Non-LTE Synthetic Observables of a<br>Multidimensional Model of Type Ia Supernovae" (Boos, Dessart, Townsley, Shen), submitted <br>to ApJ/arxiv in October 2024. This is a revised version of the dataset using an improved<br>method to generate the 1D LTE observables (1D LTE and pseudo-2D non-LTE observables have<br>thus changed from the previous version).</p> <p>We include in this dataset the 2D double detonation ejecta model (produced in Boos et al. 2021),<br>as well as the 15 wedge profiles constructed from this model, which were used in the radiative<br>transfer calculations of this work. These profiles are provided in Sedona input format. The<br>headers/columns for the 1D profiles are as follows:<br>1: Sedona model type<br>2: num_cells, rmin, ejecta time, num_isos<br>3: isos<br>4-: v_r, rho, T, X_a, X_b, etc.</p> <p>We also include the synthetic spectra and photometry from this work. These synthetic observables<br>are given for each set of calculations (1D LTE, 2D LTE, 1D non-LTE, and pseudo-2D non-LTE) between<br> -3 and +15 d from maximum light. The photometry is given in absolute magnitude in the Vega system.<br>The spectra have been rebinned such that the bins are the same between the Sedona and CMFGEN<br>calculations. The flux in these spectral files is given for a distance of 1 kpc.</p> <p><br>-Samuel J. Boos (sjboos@crimson.ua.edu)</p>
Dataset: Integral Ad Science Holding Corp. (IAS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Host Data from: [O II] as an Effective Indicator of the Dependence Between the Standardised Luminosities of Type Ia Supernovae and the Properties of their Host Galaxies
<p>Spectral properties of the Foundation Host Galaxies presented in the paper: <span>[O</span> II<span>] as an Effective Indicator of the Dependence Between the </span><span>Standardised Luminosities of Type Ia Supernovae and the Properties of </span><span>their Host Galaxies.</span></p> <p><span>Spectra were taken using the WiFeS instrument on the ANU 2.3m Telescope.</span></p>
Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (3rd version)
<p><strong>Summary</strong></p> <p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: <a href="https://doi.org/10.3847/1538-4357/aac88f">10.3847/1538-4357/aac88f</a>, <a href="https://arxiv.org/abs/1604.04631">arXiv:1604.04631</a>).</p> <p>The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper.</p> <p>To uncompress the files, the XZ Utils software package is required.</p> <p>The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification. The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers.</p> <p>Please read the README files in each package for more details and instructions.</p> <p><strong>Release notes for version 3.1</strong></p> <p>Version 3.1 is a minor revision with the addition of some alternative input parameter distributions.</p> <p><strong>Release notes for version 3</strong></p> <p>This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts.</p> <p><strong>Earlier versions</strong></p> <p>Earlier versions are available from Zenodo. Links: <a href="https://doi.org/10.5281/zenodo.49825">v1</a>, <a href="https://doi.org/10.5281/zenodo.57982">v2</a>.</p>
ESA SEOM-IAS – Measurement and ACS database SO2 UV region
<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for SO2 according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in two zip files, one for the measurements (SO2_UV_region_measurement_database_20112018.zip), one for the absorption cross sections (SO2_UV_region_absorption_cross_section_database_20112018.zip) and a readme file (ESA_SEOM_IAS_spectra_SO2UVRegion_readme.docx).</p>
Data for: Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids
<p>Data for: "Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids". The photometry provided is after DOLPHOT quality cuts, foreground extinction corrections, and spatial cuts.</p>
PLATE IA. Natula Gorochov, 1987. (A–L), Natula matsuurai (Sugimoto, 2001): A, Male; B, Female; C, Face with a transverse dark strip near epistomal suture; D, Fifth joint of maxillary palpi hatchet shaped; E, Lateral field of tegmina deeper than lateral lobe of pronotum; F, Hind tibia with 3 pairs of dorsal spines on both sides but largest inner apical spurs as long as or half of basitarsus; G, Fore tibia with oval shaped outer and inner tympanum; H, Harp vein only one, Mirror area occupying half dorsal surface, not divided with a small concentric inner veinlet; I, Pronotum with roundly convex anterior margin; J, Female ovipositor strongly upcurved, half as long as hind femur, three fifth area from base widened and bumpy, with a dorsal groove, cerci as long as ovipositor; K, Male sub-genital plate longer than wide, hind margin narrowly truncated with a small projected median lobe, two styli present; L, Female sub-genital plate roundly triangular. in JHABAR MAL, RAJENDRA NAGAR & R. SWAMINATHAN (2014) Record of Natula matsuurai Sugimoto (Orthoptera: Gryllidae: Trigonidiinae) and other sword-tailed crickets from India. Zootaxa, 3760(3): 458-462.
PLATE IA. Natula Gorochov, 1987. (A–L), Natula matsuurai (Sugimoto, 2001): A, Male; B, Female; C, Face with a transverse dark strip near epistomal suture; D, Fifth joint of maxillary palpi hatchet shaped; E, Lateral field of tegmina deeper than lateral lobe of pronotum; F, Hind tibia with 3 pairs of dorsal spines on both sides but largest inner apical spurs as long as or half of basitarsus; G, Fore tibia with oval shaped outer and inner tympanum; H, Harp vein only one, Mirror area occupying half dorsal surface, not divided with a small concentric inner veinlet; I, Pronotum with roundly convex anterior margin; J, Female ovipositor strongly upcurved, half as long as hind femur, three fifth area from base widened and bumpy, with a dorsal groove, cerci as long as ovipositor; K, Male sub-genital plate longer than wide, hind margin narrowly truncated with a small projected median lobe, two styli present; L, Female sub-genital plate roundly triangular.
Type Ia supernovae from non-accreting progenitors: data, python scripts and mesa inlists
<p>This release contains the inlists and final profiles described in: Antoniadis et al., "Type Ia supernovae from non-accreting progenitors" Mesa v. 10398</p>
Probabilistic Reconstruction of Type Ia Supernova SN 2002bo
<p><strong>SN2002bo_emulator_paper_data</strong></p> <ul> <li>The TARDIS configuration YAML file used as a template for the training spectra.</li> <li>HDF5 file of posterior samples of model parameters and their associated weights from the nested sampler.</li> </ul> <p><strong>SN2002bo_emulator_paper_spectra</strong></p> <ul> <li>Input grid of model parameters and corresponding spectra produced by TARDIS used to generate the training grid of the emulator.</li> </ul>
Statistical Test of Distance-Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (2nd version)
<p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance-Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2016, preprint, arXiv:1604.04631 https://arxiv.org/abs/1604.04631).</p> <p>An earlier version (https://zenodo.org/record/49825) has already been made available. In this updated version, we use the recently released consensus BOSS BAO measurement results (arXiv:1607.03155) as the source of angular-diameter distances. The choices of complementary cosmological parameters are expanded to include extensions to the Planck base-ΛCDM model parameters. In addition, various programming bugs are fixed.</p> <p>The compressed archive file "ddmc-nosample-v2.tar.bz2" contains only the compressed SNIa data, the BAO measurements, and the Planck chain files. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc-v2.tar.bz2" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v2.tar.bz2" file.</p> <p>The file "CHECKSUM-sha1" contains the SHA1 hash values for verifying file integrity.</p> <p>Please read the README files in each package for more details and instructions.</p>
Statistical Test of Distance-Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (1st version)
<p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance-Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2016, preprint, arXiv:1604.04631 https://arxiv.org/abs/1604.04631).</p> <p>The compressed archive file "ddmc-nosample.tar.bz2" contains only the compressed SNIa data, the BAO data parameters, and the Planck chain files. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc.tar.bz2" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample.tar.bz2" file.</p> <p>The file "CHECKSUM-sha1" contains the SHA1 hash values for verifying file integrity.</p> <p>Please read the README files in each package for more details and instructions.</p>
Effects of Type Ia Supernovae in young globular clusters
<p>Talk at Elba 23, conference in honor of Mike Rich</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.