cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p95eulti
title: Snake River Plain Play Fairway Analysis Phase 1 Favorability Model (DE EE0006733)

description: This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 1 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Brief descriptions of data layers are in the metadata of GIS files, greater detail is available in the Larger Work, the Snake River Plain Play Fairway Analysis Phase 1 report. A link to the report is available in the Related External Resources section.
DOI: 10.5066/p9y8mezy
title: Snake River Plain Play Fairway Analysis Phase 2 Favorability Model (DE EE0006733)

description: This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 2 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Phase 2 examines two subset areas of the Phase 1 study area, Mountain Home and Camas Prairie. Brief descriptions of data layers are in the metadata of GIS files, greater detail is available in the Larger Work, the Snake River Plain Play Fairway Analysis Phase 2 report. A link to the report is available in the Related External Resources section.
DOI: 10.5063/f1319svv
title: Great-tailed grackle behavioral flexibility and problem solving experiments, Santa Barbara, CA USA 2014-2015

description: Behavioral flexibility is considered an important trait for adapting to environmental change, but it is unclear what it is, how it works, and whether it is a problem solving ability. I investigated behavioral flexibility and problem solving experimentally in great- tailed grackles, an invasive bird species and thus a likely candidate for possessing behavioral flexibility. Grackles demonstrated behavioral flexibility in two contexts, the Aesop’s Fable paradigm and a color association test. Contrary to predictions, behavioral flexibility did not correlate across contexts. Four out of 6 grackles exhibited efficient problem solving abilities, but problem solving efficiency did not appear to be directly linked with behavioral flexibility. Problem solving speed also did not significantly correlate with reversal learning scores, indicating that faster learners were not the most flexible. These results reveal how little we know about behavioral flexibility, and provide an immense opportunity for future research to explore how individuals and species can use behavior to react to changing environments.
DOI: 10.5281/zenodo.7863955
title: Automated Earthquake Catalogue for SIEMBRA & ESP networks in South America (Pampean Flat Slab)

description: This repository includes: 1. A gzipped automated catalogue of earthquake locations with P and S arrival times for the SIEMBRA and ESP networks in South America 2. A README explaining the information in each column
DOI: 10.5281/zenodo.7864070
title: Automated Earthquake Catalogues for CHARGE & CHARSME/CSN networks in South America (Pampean Flat Slab)

description: This repository includes: 1. Gzipped automated catalogues of earthquake locations with P and S arrival times for: The CHARGE network - charge.data.gz The CHARSME and CSN networks - charsme_csn.data.gz 2. A README explaining the information in each column
DOI: 10.5281/zenodo.7992039
title: CONUS NG-IDF 2.0: Shrub

description: The <strong>NG-IDF: Shrub</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Shrub</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Shrub_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7963584
title: CONUS NG-IDF 2.0: Crop

description: The <strong>NG-IDF: Crop</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Crop</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Crop_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7972064
title: CONUS NG-IDF 2.0: Deciduous Forest

description: The <strong>NG-IDF: Deciduous Forest</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Deciduous Forest</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Deciduous_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7976419
title: CONUS NG-IDF 2.0: Evergreen Forest

description: The <strong>NG-IDF: Evergreen Forest</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Evergreen Forest</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Evergreen_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7982399
title: CONUS NG-IDF 2.0: Open Area

description: The <strong>NG-IDF: Open Area</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Open Area </strong>(i.e., no vegetation) land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Open_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7707336
title: Genomically predicted theoretical protein mass database for mass spectrometry (GPMsDB) evaluation datasets

description: These are datasets obtained for the evaluation of GPMsDB (genomically predicted protein mass database) and its toolkits (GPMsDB-tk/GPMsDB-dbtk). The following datasets are deposited. The genome sequences of the strains newly sequenced and added using GPMsDB-dbtk (genomes_added.zip) MALDI-TOF-MS peak lists obtained from reference bacterial and archaeal strains (MALDI_peaklists.zip) 16S rRNA gene sequences of the faecal isolates (mice_isolates_16S_nanopore.zip) Metagenome-assembled genomes from mouse faeces (mice_MAGs.zip)
DOI: 10.6084/m9.figshare.16557387.v1
title: ECCOv4r4 Tropical Pacific Velocity Fields

description: Hourly horizontal velocity fields at select longitudes and depths in the Tropical Pacific, computed from the ECCOv4r4 state estimate. The code to extract the fields from a rerun of ECCOvr4r4 can be found here: https://github.com/timothyas/eccov4-euc<br>A description of the ECCOv4r4 setup and data can be found here: <br>ECCO Consortium, Fukumori, Ichiro, Wang, Ou, Fenty, Ian, Forget, Gael, Heimbach, Patrick, & Ponte, Rui M. (2021). Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate, Version 4 Release 4 (4 Release 4). Zenodo. https://doi.org/10.5281/zenodo.4533349 <br><br>
DOI: 10.5281/zenodo.7978021
title: CONUS NG-IDF 2.0: Grassland

description: The <strong>NG-IDF: Grassland</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Grassland</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Grass_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7995995
title: CONUS NG-IDF 2.0: Wetland

description: The <strong>NG-IDF: Wetland</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Wetland</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Wetland_readmefirst.txt" file for details.
DOI: 10.6084/m9.figshare.5545267
title: supplement information-tables.xlsx

description: Table 1 presents the raw data on rodent captured during 4 years. Their abundance is evaluated through the trapping success, which is based on the numbers by trapping night.<br><br>Table 2 presents the bait consumption in grammes per day (J1 to J31) during year 3. <br><br>Table 3 presents the bait consumption in grammes per day (J1 to J31) during year 4.
DOI: 10.6073/pasta/ca866e3441ce3ed20f79bf43741bf181
title: North Temperate Lakes LTER: High Frequency Meteorological and Dissolved Oxygen Data - Trout Lake Buoy 2004 - current

description: The instrumented buoy on Trout Lake is equipped with a dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are usually collected every 10 minutes with occasional periods of 2 minute data for short periods to answer specific questions. The D-Opto dissolved oxygen sensor is 0.5m from the lake surface. Meteorological sensors measure wind speed, wind direction, relative humidity, air temperature, photosynthetically active radiation (PAR), and barometric pressure. Starting in 2005, thermistors were placed every 0.5-1m from the surface through 14m and every 2 to 4m from 14m to the bottom of the water column at 31m. In July 2006, a new thermistor chain was deployed with thermistors placed every meter from the surface through a depth of 19 meters. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Data are averaged to daily values from one minute samples for years 2005 - 2006. Daily values are computed from high resolution data starting in year 2007. Data are averaged to hourly values from one minute samples for years 2005 - 2008, Hourly values are computed from high resolution data starting in year 2009. Hourly and daily values may not be current with high resolution data in the current year. Sampling Frequency: varies for instantaneous sample. averaged to hourly and daily values from one minute samples Number of sites: 1
DOI: 10.5281/zenodo.8245428
title: Genomically predicted theoretical protein mass database for mass spectrometry (GPMsDB) R01-RS95

description: GPMsDB-tk/GPMsDB-dbtk are software toolkits for assigning taxonomic identification to user-provided MALDI-TOF mass spectrometry profiles obtained from bacterial and archaeal cultured isolates. This is a database (release R01-RS95) used for the toolkits. This database is released under the Creative Commons Attribution-ShareAlike 4.0 International License. GPMsDB-tk/GPMsDB-dbtk are available at https://github.com/ysekig. Details are shown at the GitHub sites.
DOI: 10.5281/zenodo.6898244
title: Unlabeled Empatica E4 Wristband Data (UE4W) Dataset

description: The UE4W dataset was recorded over a period of approximately two weeks by a singe adult male. We found that keeping accurate records and labeling were impractical so it was archived. However, recent advances in representation and unsupervised learning have led us to look at it again. The dataset contains over 250 hours of recordings using an Empatica E4 wristband. In general the longer files are daytime recordings, the shorter ones are nighttime. The tags indicate the beginning and end of an event change (E.g., getting up to go for walk) however the lack of a tag is not a meaningful indicator. A Jupyter notebook is provided which processes each file into a Pandas dataframe and generates a windowed plot of the total acceleration. Additional and updated code may be found on our IMICS lab github. Several reference screenshots from the Empatica Connect Website are also included.
DOI: 10.6073/pasta/db38add6b67134d205086de3d7366001
title: North Temperate Lakes LTER: High Frequency Water Temperature Data - Trout Bog Buoy 2003 - 2014

description: The instrumented buoy on Trout Bog is equipped with a thermistor chain that measures water temperature from depths ranging from the surface to 7m placed every 0.5-1m throughout the water column. The surface temperature sensors are attached to floats so that they are as close to the surface as feasible. The Trout Bog buoy is also equipped with a dissolved oxygen sensor and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are usually collected every 10 minutes with occasional periods of 2 minute data for short periods to answer specific questions. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Sampling Frequency: varies for instantaneous sample. averaged to hourly and daily values from one minute samples Number of sites: 1
DOI: 10.25318/1310011101-eng
title:  Number and rates of new cases of primary cancer, by cancer type, age group and sex

description: Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.
DOI: 10.5281/zenodo.7980090
title: CONUS NG-IDF 2.0: Mixed Forest

description: The <strong>NG-IDF: Mixed Forest</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Mixed Forest</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Mixed_readmefirst.txt" file for details.
DOI: 10.5281/zenodo.7510186
title: Microwave Single Scattering Properties Database (Horizontally Aligned Aggregates of Dendrites)

description: The database contains physical and microwave single scattering properties of horizontally aligned frozen hydrometeors as large as 11 cm in diameter. A description of the aggregation model used for particle generation can be found in:<br> Leinonen, J., and Szyrmer, W. (2015), Radar signatures of snowflake riming: A modeling study, <em>Earth and Space Science</em>, 2, 346– 358, doi:10.1002/2015EA000102.<br> The code used for particle generation is freely available at: https://github.com/jleinonen/aggregation The scattering properties of particles were computed using discrete dipole approximation using ADDA software package (https://github.com/adda-team/adda) Terminal velocity of snowflakes was computed using 4 hydrodynamical models that were implemented as a part of snowScat library (https://github.com/OPTIMICe-team/snowScatt) Approximately one half of the snowflake structure files and one quarter of scattering properties (for X, Ku, Ka and W band) were generated for the publication of Leinonen and Szyrmer (2015). The remaining part of the dataset was generated using the ALICE High Performance Computing Facility at the University of Leicester.
DOI: 10.5281/zenodo.7983633
title: CONUS NG-IDF 2.0: Pasture

description: The <strong>NG-IDF: Pasture</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Pasture</strong> land use land cover (LULC). These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale. They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties. See the "Pasture_readmefirst.txt" file for details.
DOI: 10.6073/pasta/a457e305538a0d8e669b58bb6f35721f
title: North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Major Ions 1981 - current

description: Parameters characterizing the major ions of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish) are measured at one station in the deepest part of each lake at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include chloride, sulfate, calcium, magnesium, sodium, potassium, iron, manganese, and specific conductance (northern lakes only). Sampling Frequency: quarterly (winter, spring and fall mixis, and summer stratified periods) Number of sites: 11
DOI: 10.6073/pasta/c03b39550e79d002d82a2281f8546c78
title: North Temperate Lakes LTER: High Frequency Data: Meteorological, Dissolved Oxygen, Chlorophyll, Phycocyanin - Lake Mendota Buoy 2006 - current

description: The instrumented buoy on Lake Mendota is equipped with a dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are collected every minute. Hourly and daily averages for water temperatures are derived from the high resolution data. The D-Opto dissolved oxygen sensor is 0.5m from the lake surface. In 2006, the thermistors were placed every 0.5 m from the surface through 7m and every 1m from 7m to 15m. In 2007 and 2008, the thermistors were placed every 0.5 m from the surface through 2m and every 1m from 2m to 20m. Meteorological sensors measure wind speed, wind direction, relative humidity, air temperature, photosynthetically active radiation (PAR), and barometric pressure. Not all sensors are deployed each season. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. The instrumented buoy was deployed in Lake Mendota in 2006 from June 27 to October 17. In 2007, from May 18 to October 29. In 2008, from 26 Jun to 14 Nov. In 2009, the buoy was deployed from April 17 to Nov 23. In 2009+, the Mendota buoy was located at 43.0995, -89.4045. 2017 notes: the buoy was deployed from Apr 22 to November 13. A boating mishap caused the loss of the air temp, humidity, and wind sensors between May 28 and July 11. The dissolved oxygen sensor had significant biofouling from algae and at least one zebra mussel during the year. In 2018 the buoy was deployed from April 11 to November 15. The dissolved oxygen sensor was a PME MiniDOT logger, having replaced a D-Opto sensor that was used since 2006. In 2019, the buoy was operating from April 16 to November 6th. A YSI Exo2 sonde was added to the buoy including dissolved oxygen (replacing the D-Opto), chlorophyll, phycocyanin, specific conductance, pH, fdom, and turbidity sensors. The chlorophyll and phycocyanin sensors are replacing older Turner sensors used in previous years. Both sets output RFU, but have a significant difference in magnitude. The YSI pH, DO, and specific conductance sensors were cleaned and calibrated about every two weeks. The sampling frequency is one minute. Number of sites: 1
DOI: 10.6084/m9.figshare.21266562
title: in vivo quantitative FRET small animal imaging: intensity versus lifetime-based FRET

description: This data repository is associated with the article titled "in vivo quantitative FRET small animal imaging: intensity versus lifetime-based FRET}". Each folder below includes IVIS (intensity) and MFLI (time-resolved gated-ICCD) data and results in seperate folders - each of which will have a more specialized ReadMe file for details. <br> - 'Fig 1 - dsDNA/' : All data and results pertaining to dsDNA figure 1. <br> - 'Figs 2-5 & S3-S4, S7-S9: Tf-TfR FRET in vivo/': All data and results pertaining to Transferrin kinetics experiment in vivo. <br> - Fig. S5-S6: Data and results corresponding to "control" mice injected with AF700-Tf (donor) only. <br> Please contact the corresponding authors (michalet@chem.ucla.edu, barrosM@amc.edu) or Jason Smith (jasontsmith2718@gmail.com) for additional information.
DOI: 10.5281/zenodo.7434298
title: A supramolecular cucurbit[8]uril-based rotaxane chemosensor for the optical tryptophan detection in human serum and urine

description: Dataset of the publication data. Raw and analyzed data included in each excel file.