cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13139/ornlnccs/2472925
title: Road Lidar Dataset for the TxDOT Austin District

description: This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.
DOI: 10.18138/2474912
title: Public Data Set: Erratum: “Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection” [Phys. Plasmas 28, 102504 (2021)]

description: This public data set contains openly-documented, machine readable digital research data corresponding to figures published in G.M. Bodner et al., ‘Erratum: “Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection” [Phys. Plasmas 28, 102504 (2021)],’ Physics of Plasmas 31, 129904 (2024).
DOI: 10.7481/2478402
title: Reverse Osmosis Simulation Data

description: This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to the "Related Work" resource below for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.
DOI: 10.6073/pasta/0d6b08fbcf08605881edfb7acf0a1741
title: Genome size influences plant growth and biodiversity responses to nutrient fertilization in diverse grassland communities

description: Experiments comparing diploids with polyploids and in single grassland sites show that nitrogen and/or phosphorus availability influences plant growth and community composition dependent on genome size; specifically plants with larger genomes grow faster under nutrient enrichments relative to those with smaller genomes. However, it is unknown if these effects are specific to particular site localities with speciifc plant assemblages, climates, and historical contingencies. To determine the generality of genome size dependent growth responses to nitrogen and phosphorus fertilisation, we combined genome size and species abundance data from 27 coordinated grassland nutrient addition experiments in the Nutrient Network that occur in the Northern Hemisphere across a range of climates and grassland communities. We found that after nitrogen treatment, species with larger genomes generally increased more in cover compared to those with smaller genomes, potentially due to a release from nutrient limitation. Responses were strongest for C3 grasses and in less seasonal, low precipitation environments, indicating that genome size effects on water-use-efficiency modulates genome size-nutrient interactions. Cumulatively the data suggest that genome size is informative and improves predictions of species’ success in grassland communities.
DOI: 10.15468/dl.5dtvez
title: Occurrence Download

description: A dataset containing 401 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation)",
 "DatasetKey is Observation.org, Nature data from around the World",
 "GadmGid is CAN.2_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2000-2025"
 ]
}

 The dataset includes 401 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055491-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ghrzyf
title: Occurrence Download

description: A dataset containing 314 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation)",
 "DatasetKey is one of (naturgucker, Earth Guardians Weekly Feed, NaGISA Project)",
 "GadmGid is CAN.2_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2000-2025"
 ]
}

 The dataset includes 314 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055492-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/9d4c803578c3fbcb45fc23f13124d052
title: Data Package Design for Special Cases

description: In this document we consider special cases for archiving research data based on their data type, format, or acquisition method, and recommend practices that ensure optimal re-usability of the data. Most recommendations here are aimed at improving documentation of data acquisition and processing to avoid misinterpretation. This includes the recommendation to publish raw data and/or processing code along with the data products. Others are aimed at usability in terms of data size/volume or connecting related data. Some recommendations involve including a metadata document formatted according to a new and emerging standard (e.g., codeMeta) or a data inventory table. Data inventory tables can cross the line between metadata and data and are intended to improve discoverability and navigation of archived data.
DOI: 10.21947/2484324
title: WREF field profiling lidar

description: The field profiling lidar #ZX300-1945 was deployed on the ground in an open field 880 m south of the NEON tower and tower profiling lidar (ZX300-1381). The zip files contain high frequency data. The excel files are 10-min averages.
DOI: 10.21947/2484328
title: WREF tower profiling lidar

description: The tower profiling lidar #ZX300-1381 was deployed on top of the 74 m tall WREF NEON tower. The zip files contain high frequency data. The excel files are 10-min averages.
DOI: 10.25582/data.2025-01.3341770/2484343
title: Data for EMSL Project 60880 from January 2025
DOI: 10.25582/data.2025-01.3341787/2484344
title: Data for EMSL Project 60880 from January 2025
DOI: 10.25582/data.2025-01.3341783/2484345
title: Data for EMSL Project 60880 from January 2025
DOI: 10.25582/data.2025-01.3341725/2484346
title: Data for EMSL Project 60921 from January 2025
DOI: 10.25582/data.2025-01.3341754/2484347
title: Data for EMSL Project 60921 from January 2025
DOI: 10.25582/data.2025-01.3341777/2484348
title: Data for EMSL Project 60921 from January 2025
DOI: 10.25582/data.2025-01.3341745/2484349
title: Data for EMSL Project 60921 from January 2025
DOI: 10.15468/dl.39ynev
title: Occurrence Download

description: A dataset containing 954076 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Dominican Republic"
 ]
}

 The dataset includes 954076 records from 933 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055470-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2025-01.3341792/2484350
title: Data for EMSL Project 60921 from January 2025
DOI: 10.25582/data.2025-01.3341760/2484351
title: Data for EMSL Project 60921 from January 2025
DOI: 10.25582/data.2025-01.3342157/2484352
title: Data for EMSL Project 60846 from January 2025
DOI: 10.25582/data.2023-12.2995878/2484354
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995890/2484355
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995892/2484356
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995921/2484357
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995928/2484358
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995929/2484359
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2995949/2484360
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996505/2484361
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996519/2484362
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996549/2484363
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996558/2484364
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996569/2484365
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996602/2484366
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996613/2484367
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996627/2484368
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996650/2484369
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996665/2484370
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996683/2484371
title: Data for EMSL Project 60201 from December 2023
DOI: 10.15468/dl.2dfy4m
title: Occurrence Download

description: A dataset containing 55147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Betula L."
 ]
}

 The dataset includes 55147 records from 253 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055493-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-12.2996707/2484372
title: Data for EMSL Project 60201 from December 2023
DOI: 10.15468/dl.zfvu68
title: Occurrence Download

description: A dataset containing 12078 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Algunas especies de anfibios y reptiles contenidos en el Proyecto de Norma Oficial Mexicana PROY-NOM-059-ECOL-2000"
 ]
}

 The dataset includes 12078 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055494-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-12.2996720/2484373
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996729/2484374
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996760/2484375
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996770/2484376
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996776/2484377
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996792/2484378
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996802/2484379
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996806/2484380
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996819/2484381
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996829/2484382
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996834/2484383
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2996872/2484384
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997060/2484385
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997132/2484386
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997152/2484387
title: Data for EMSL Project 60201 from December 2023
DOI: 10.6073/pasta/a9169d7b4b17965bfef96c9a65410221
title: White Cedar Ring Width Data from Michigan Upper Peninsula, 1869-2022

description: This project used tree cores to reconstruct growth over time and correlate that with climate variables including maximum monthly temperature, monthly precipitation, and vapor pressure deficit.
DOI: 10.25582/data.2023-12.2997159/2484388
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997170/2484389
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997185/2484390
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997196/2484391
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997206/2484392
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997219/2484393
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997234/2484394
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997240/2484395
title: Data for EMSL Project 60201 from December 2023
DOI: 10.57841/casdc.0004479
title: 2022-2023年阿尔泰山跨境保护地土地利用改变数据集
DOI: 10.25582/data.2023-12.2997258/2484396
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997272/2484397
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997278/2484398
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997288/2484399
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997298/2484400
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997304/2484401
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997316/2484402
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997323/2484403
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997330/2484404
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997345/2484405
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997355/2484406
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997360/2484407
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997371/2484408
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997395/2484409
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997412/2484410
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997431/2484411
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997447/2484412
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997450/2484413
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2023-12.2997465/2484414
title: Data for EMSL Project 60201 from December 2023
DOI: 10.6073/pasta/e864c0dd4ff09fb20d6c8b14def6b798
title: Ecohydrological monitoring of urban ecological infrastructure at the Arizona State University Tempe Campus (Tempe, Arizona)

description: Urban Ecological Infrastructure (UEI) are urban ecological structures
 whose ecological functions are increasingly being used to deliver key
 urban services and address urban sustainability challenges. The
 growing use of UEI to address urban sustainability challenges can
 bring together teams of urban researchers and practitioners to
 co-produce UEI design, monitoring and maintenance. However, this
 co-production process received little attention in the literature, and
 has not been studied in the Phoenix Metro Area.

 
 This study examined several components of a co-produced design process
 and related project outcomes associated with a small-scale UEI project
 – bioretention basins installed at the Arizona State University (ASU)
 Orange Mall and Student Pavilion in Tempe, AZ. We explored the design
 process associated with the collaborative development of an
 ecohydrological monitoring protocol for assessment of
 post-construction performance of this UEI system. This co-production
 process involved both CAP LTER researchers and practitioners,
 designers, and managers at ASU and associated third-party consultants
 involved in site design and management. Together, these researchers
 and practitioners co-produced a suite of ecohydrological metrics to
 monitor the performance of the bioretention basins at Orange Mall.
 Specifically, this protocol evaluated the performance of the UEI
 system with regards to two of it’s key design goals: storm water
 capture and storm water quality improvement. The ecohydrological data
 produced by the implementation of this monitoring protocol are
 presented here. Monitoring equipment were installed throughout the
 site in June and July 2018. Site monitoring and data collection began
 in August 2018, and continued through Feb 2019.
DOI: 10.25582/data.2023-12.2997474/2484415
title: Data for EMSL Project 60201 from December 2023
DOI: 10.25582/data.2024-01.2997479/2484416
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997491/2484417
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997500/2484418
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997505/2484419
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997514/2484420
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997525/2484421
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997529/2484422
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997536/2484423
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997541/2484424
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997547/2484425
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997552/2484426
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997557/2484427
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997564/2484428
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997584/2484429
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997592/2484430
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997596/2484431
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2024-01.2997600/2484432
title: Data for EMSL Project 60201 from January 2024
DOI: 10.25582/data.2023-12.2995937/2484433
title: Data for EMSL Project 51287 from December 2023
DOI: 10.6073/pasta/fb18b84f7e5e9d9f23f224e9e6158572
title: Missouri Lakes and Reservoirs Long-term Limnological Dataset, 1976-2018.

description: This data set compiles 43 years of limnological data from Missouri lakes and reservoirs collected by the University of Missouri Limnology Lab. Although the dataset includes information from nine different projects, the bulk of the data (~75%) come from the Statewide Lake Assessment Project and the Lakes of Missouri Volunteer Program, both of them funded primarily by Missouri Department of Natural Resources. The Statewide Lake Assessment Project began in 1978 sampling a small set of reservoirs. In 1989 the assessment expanded to include regular annual summer monthly collections between May and August, though monitoring was extended for some reservoirs in certain years. We monitored 240 lakes to create the dataset, which represents over 2600 lake-years. The Lakes of Missouri Volunteer Program began in 1992 monitoring 5 lakes and reservoirs and has expanded to 121 sites on 65 waterbodies. Volunteer community scientists monitor their respective sites approximately 8 times per season (April through September). This dataset represents over 15,000 sample events. Lake Ozarks is a long-term (1976-2014) spatial examination of a single large reservoir during summer. Table Rock Monitoring is another multi-year (1995-2009) spatial examination of a large reservoir, but includes year-round data. The rest of the projects included in the dataset monitored Missouri lakes and reservoirs at various intervals including daily (Woodrail, Daily), weekly (icubed), and biweekly (High Res).
DOI: 10.25582/data.2023-12.2995940/2484434
title: Data for EMSL Project 51287 from December 2023