This file is an example data set from the Central Valley of California from a drought study corresponding to “recent non-drought conditions” (Scenario 1 in Petrie et al., in review). In 2014, following an 8-year period with 7 below-normal to critically-dry water years, the bioenergetic model TRUEMET was used to assess the impacts of drought on wintering waterfowl habitat and bioenergetics in the Central Valley of California. The goal of the study was to assess whether available foraging habitats could provide enough food to support waterfowl populations (ducks and geese) under a variety of climate and population level scenarios. This information could then be used by managers to adapt their waterfowl habitat management plans to drought conditions. The study area spanned the Central Valley and included the Sacramento Valley in the north, the San Joaquin Valley in the south, and Suisun Marsh and Sacramento-San Joaquin River Delta (Delta) east of San Francisco Bay. The data set consists of two foraging guilds (ducks and geese/swans) and five forage types: harvested corn, rice (flooded), rice (unflooded), wetland invertebrates and wetland moist soil seeds. For more background on the data set, see Petrie et al. in review.

Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Auxiliary Meteorology Data for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET) contains GEOS-IT analyses and VIIRS satellite data that are subsets and interpolations corresponding to data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual PREFIRE CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_AUX-MET contains surface and skin temperatures, land fraction, sea ice concentration, snow cover, surface pressure, temperature profiles, pressure profiles, O3 profiles, wind velocity profiles, and surface type. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The auxiliary meteorology data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_AUX-MET collection.

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

This dataset provides annual climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable. Each data file is provided as a single year by variable and covers the same period of record as the Daymet V4 R1 daily data. The annual climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Separate annual files are provided for the land areas of continental North America, Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (.nc) and Cloud Optimized GeoTIFF (.tif) file formats. In Version 4 R1, all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

The dataset contains records of the user's Spotify streaming history, with each row representing a specific instance of a played track. The data includes various attributes providing insights into the user's music listening habits.

Columns:

1. ts (Timestamp):

   • The timestamp when the track was played.

2. platform:

   • The platform or device used for streaming (e.g., Windows 10).

3. ms_played:

   • The duration in milliseconds of how long the track was played.

4. conn_country:

   • The country code indicating the user's location during streaming (e.g., LK for Sri Lanka).

5. master_metadata_track_name:

   • The name of the track played.

6. master_metadata_album_artist_name:

   • The artist of the album to which the track belongs.

7. master_metadata_album_album_name:

   • The name of the album containing the track.

8. spotify_track_uri:

   • The unique Spotify URI for the track.

9. reason_start:

   • The reason for starting the track (e.g., play button clicked).

10. reason_end:

    • The reason for ending the track (e.g., track done).

11. shuffle:

    • Indicates whether shuffle mode was enabled (True/False).

12. offline:

    • Indicates whether the track was played offline (True/False).

13. offline_timestamp:

    • Timestamp indicating when the track was played offline (if applicable).

14. incognito_mode:

    • Indicates whether incognito mode was enabled (True/False).

Purpose:

This dataset is suitable for performing detailed Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights into the user's music-listening behaviour. Potential analyses could include the distribution of listening durations, favourite artists and tracks, exploration of geographic listening patterns, and examination of usage patterns across different platforms.

Visualization tools such as Matplotlib and Seaborn could be utilized for a more in-depth analysis to create visual representations of the findings. This dataset aligns well with your interest in data science, offering opportunities to apply analytical techniques to real-world streaming data.

This dataset contains the International Surface Pressure Databank version 4.7 (ISPDv4), the world's largest collection of pressure observations. It has been gathered through international cooperation with data recovery facilitated by the ACRE Initiative and the other contributing organizations and assembled under the auspices of the GCOS Working Group on Surface Pressure and the WCRP/GCOS Working Group on Observational Data Sets for Reanalysis by NOAA Earth System Research Laboratory (ESRL), NOAA's National Climatic Data Center (NCDC), and the University of Colorado's Cooperative Institute for Research in Environmental Sciences (CIRES). The ISPDv4 consists of three components: station, marine, and tropical cyclone best track pressure observations. The station component is a blend of many national and international collections. In addition to the pressure observations and metadata, ISPDv4 contains feedback from the 20th Century Reanalysis version 3, including quality control information and uncertainty information.

Support for the International Surface Pressure Databank is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.

The International Surface Pressure Databank version 4.7 and 20th Century Reanalysis version 3 used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

This file (wymt_exffa_2015_WATSTORE.txt) contains peak flow data for 99 selected streamgages in or near Montana. The file is in a text format called WATSTORE (National Water Data Storage and Retrieval System) available from NWISWeb (http://nwis.waterdata.usgs.gov/usa/nwis/peak).

This dataset contains Facebook comments about Netflix or on Netflix posts. It can have various uses around sentiment analysis or training of large language models.

While data is scraped using API as json or column based relational data, its very important to note that most language models do better understanding on sentences with a continuous narrative that json and relational data in their original format, hence the need to have that data into sentences first before input into LLM models for further processing

Original data from: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation. Changes made: - the class column (1) was binarised to -1=unacc, +1=acc (including acc/good/vgood) - A subsample of 390 records was used, because the original 1728 records were an exhaustive expression of the feature space and also too computationally demanding. Attributes 1. CLASS: Acceptability (0=unacc, 1=acc, 2=good, 3=v.good) NOTE: ALT version is binarised to -1 unacc, +1 acc/good/vgood 2. Price (3=v-high,2= high, 1=med, 0=low) 3. Maint (3=v-high,2= high, 1=med, 0=low) 4. Doors (2,3,4,5 (or more)) 5. Persons (2,4,6 (or more)) 6. Luggage (0=small,1= med, 2=big) 7. Safety (0=low,1= med, 2=big)

California Water Code Section 10004.6 requires the California Department of Water Resources (DWR) to quantify current and future water conditions in the state. This information is published in the California Water Plan (Water Plan), which is updated every five years. Water Plan Updates 2005, 2009, 2013, 2018 and 2023 have progressively developed a Water Evaluation And Planning (WEAP) model for assessing the impacts of climate change on California water resources and infrastructure, as well as the adaptation strategies needed and available to improve regional water resilience. Update 2023 brought significant improvements to input data and process in the model as described in the model documentation provided in the resources. The current WEAP model is called the WEAP-CVPA model as it covers the Central Valley of California at the planning area scale. The model was run on WEAP version 2021.0.2.2 but is compatible with newer versions of WEAP based on limited testing.

Included in this dataset is:

• The model itself with input files provided as a zip file
• A link to SEI’s website where the software to run and view the model can be downloaded
• The documentation for the model that was released as part of the California Water Plan Update 2023
• 4 CSVs with the post processed data from the study that were used to create the Future Scenarios Interactive Data Explorer, separated by summary vs detailed response surface as well as by planning area metrics vs points of interest metrics
• A link to the Future Scenarios Interactive Data Explorer where the post processed results and response surfaces available at the planning area level
• The raw exports from the model of the level 2070 that were post processed to create the data that informed the Water Plan Update 2023
• Spatial Boundaries for Water Plan Planning Areas which were used as the basis for spatial areas in the WEAP model

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

This training dataset was calculated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Diagnosis and Prognosis" (Energies, under review) for more details

The V vs. Q dataset was compiled with a resolution of 0.01 for the triplets and C/25 charges. This accounts for more than 5,000 different paths. Each path was simulated with at most 0.85% increases for each The training dataset, therefore, contains more than 700,000 unique voltage vs. capacity curves.

4 Variables are included, see read me file for details and example how to use. Cell info: Contains information on the setup of the mechanistic model Qnorm: normalize capacity scale for all voltage curves pathinfo: index for simulated conditions for all voltage curves volt: voltage data. Each column corresponds to the voltage simulated under the conditions of the corresponding line in pathinfo.

Laos Exports of machinery for working rubber or plastics to Japan was US$13.78 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Laos Exports of machinery for working rubber or plastics to Japan - data, historical chart and statistics - was last updated on November of 2025.

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

V(PO3)4 crystallizes in the monoclinic C2/c space group. The structure is three-dimensional. V4+ is bonded to six O2- atoms to form VO6 octahedra that share corners with six PO4 tetrahedra. There are a spread of V–O bond distances ranging from 1.89–2.02 Å. There are two inequivalent P5+ sites. In the first P5+ site, P5+ is bonded to four O2- atoms to form PO4 tetrahedra that share corners with two equivalent VO6 octahedra and corners with two equivalent PO4 tetrahedra. The corner-sharing octahedra tilt angles range from 30–47°. There are a spread of P–O bond distances ranging from 1.50–1.60 Å. In the second P5+ site, P5+ is bonded to four O2- atoms to form PO4 tetrahedra that share a cornercorner with one VO6 octahedra and corners with two equivalent PO4 tetrahedra. The corner-sharing octahedral tilt angles are 44°. There are a spread of P–O bond distances ranging from 1.45–1.64 Å. There are six inequivalent O2- sites. In the first O2- site, O2- is bonded in a bent 150 degrees geometry to one V4+ and one P5+ atom. In the second O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one V4+ and one P5+ atom. In the third O2- site, O2- is bonded in a bent 120 degrees geometry to two P5+ atoms. In the fourth O2- site, O2- is bonded in a distorted bent 120 degrees geometry to one V4+ and one P5+ atom. In the fifth O2- site, O2- is bonded in a bent 150 degrees geometry to two P5+ atoms. In the sixth O2- site, O2- is bonded in a single-bond geometry to one P5+ atom.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

This dataset represents percent area consisting of carbonate-rock aquifers, igneous and metamorphic-rock, sandstone, sandstone and carbonate-rock, semiconsolidated sand, and unconsolidated sand and gravel aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This post contains the stimuli texts, data, and R code for analysis for this article. Here is the abstract for the article: The relationship between construal and redundancy has not been previously explored empirically. Russian aspect allows speakers to construe situations as either Perfective or Imperfective, but it is not clear to what extent aspect is determined by context and therefore redundant. We investigate the relationship between redundancy and open construal by surveying 500 native Russian speakers who rated the acceptability of both Perfective and Imperfective verb forms in complete extensive authentic contexts. We find that aspect is largely redundant in about 81% of uses, and in about 17% of contexts aspect is relatively open to construal. We contend that anchoring in redundant contexts likely facilitates the independence of construal in contexts with less redundancy. However further research is needed to discover what makes contexts redundant since known cues for aspect are absent in the majority of such contexts. Native speakers are fairly consistent in giving the original aspect high ratings, but less consistent in rating the non-original aspect, indicating potential problems in testing the reactions of speakers to non-authentic data.

This data product consists of measurements from rate counters. The rate count data are diagnostic fields, have uncalibrated energy ranges, and wrap near or above 16384 counts/s. The rate count values are stored as 4 s accumulations of counts.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.