cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60593/ur.d.29161688.v1
title: San Juan Basin flux data from "Bottom-up characterization of geologic methane emissions in the San Juan Basin in the southwestern USA"

description: Atmospheric methane is the second most important anthropogenic greenhouse gas and a key contributor to atmospheric chemistry and global warming. Significant uncertainties remain in the global methane budget, particularly regarding emissions from natural geologic sources. This study provides a detailed bottom-up characterization of natural geologic methane emissions from the San Juan Basin, a high-seepage hydrocarbon basin located in southern Colorado and northern New Mexico. The dataset includes methane, carbon dioxide and ethane flux measurements across the San Juan Basin. Seepage was measured using a dynamic closed-system flux chamber, first developed by Kazemi et al. (2021), connected to portable greenhouse gas analyzers (Los Gatos Research Ultraportable Greenhouse Gas Analyzer (“LGR”) or a Picarro G4302 Mobile Gas Concentration Analyzer (“Picarro”)). Fluxes were calculated using Equation 1 in Kazemi et al. (2021). The d[CH4]/dt is found by taking the average of slopes of 1-minute segments of the concentration stepping every 10 seconds. The uncertainty associated with d[CH4]/dt is the standard error of these slopes.<br>Our results show that natural geologic emissions are limited to the Fruitland coal outcrop and are associated with coal cleats, faults, and/or areas of hydrodynamic overpressure. Significant positive fluxes were observed on the Fruitland outcrop, particularly in the northwestern region.<br>The code used to produce these calculated fluxes is available at https://github.com/KatieHall1919/Flux-Calculation/tree/main. This work is associated with "Bottom-up characterization of geologic methane emissions in the San Juan Basin in the southwestern USA" by Margaret R. Scholer, Kathleen R. Hall, Thomas S. Weber, Marc L. Buursink, Mingzhe Zhu, Alexander C. Ihle, Devin Hencmann, Andrew M. Smith, Katey M. Walter Anthony, and Vasilii V. Petrenko<br>ReferencesKazemi, R., Schlageter, W., Hmiel, B., Weber, T.S., Murray, L.T., Petrenko, V.V., 2021. Investigating methane emissions from geologic microseepage in Western New York State, United States. Elem. Sci. Anthr. 9, 00066. https://doi.org/10.1525/elementa.2020.00066
DOI: 10.17876/musewide/dr.1/18312
title: Object GUO_ID 18312 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29392067
title: Raw data files for paper on permeation

description: Raw data files for a permeation paper being submitted to Physical Review Materials.
DOI: 10.60593/ur.d.29392067.v1
title: Raw data files for paper on permeation

description: Raw data files for a permeation paper being submitted to Physical Review Materials.
DOI: 10.17876/musewide/dr.1/18315
title: Object GUO_ID 18315 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29410703
title: Demographic information of participants, their PD diagnosis, screening labels provided by clinicians, and screening predictions made by PARK

description: Data dictionary:date: date of data collection;protocol: tag for eight different study protocols used to recruit participants;test_split: global (balanced test data), validation_1 (external evaluation supervised), validation_2 (external evaluation unsupervised);pred_score_smile: predicted probability of having PD based on the smile-task model;pred_score_speech: predicted probability of having PD based on the speech-task model;pred_score_finger: predicted probability of having PD based on the finger-tapping-task model;pred_score_fusion: predicted probability of having PD provided by the PARK tool;true_label: 1 (with PD), 0 (without PD);uncertain_flag: True (PARK was uncertain about the prediction), False (not uncertain);neurologist_label_N1/2/3: 1 (clinician 1/2/3 thought individuals had signs of PD), 0: non-PD
DOI: 10.60593/ur.d.29410703.v1
title: Demographic information of participants, their PD diagnosis, screening labels provided by clinicians, and screening predictions made by PARK

description: Data dictionary:date: date of data collection;protocol: tag for eight different study protocols used to recruit participants;test_split: global (balanced test data), validation_1 (external evaluation supervised), validation_2 (external evaluation unsupervised);pred_score_smile: predicted probability of having PD based on the smile-task model;pred_score_speech: predicted probability of having PD based on the speech-task model;pred_score_finger: predicted probability of having PD based on the finger-tapping-task model;pred_score_fusion: predicted probability of having PD provided by the PARK tool;true_label: 1 (with PD), 0 (without PD);uncertain_flag: True (PARK was uncertain about the prediction), False (not uncertain);neurologist_label_N1/2/3: 1 (clinician 1/2/3 thought individuals had signs of PD), 0: non-PD
DOI: 10.17876/musewide/dr.1/18316
title: Object GUO_ID 18316 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29525180
title: 2021 Fe-MRI Data

description: 2021 Fe MRI Data
DOI: 10.60593/ur.d.29525180.v2
title: 2021 Fe-MRI Data

description: 2021 Fe MRI Data
DOI: 10.15468/dl.je6cm5
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corispermum pallasii",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 44 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011845-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/18317
title: Object GUO_ID 18317 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29525180.v1
title: 2021 Fe-MRI Data

description: 2021 Fe MRI Data
DOI: 10.17876/musewide/dr.1/18318
title: Object GUO_ID 18318 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29636924
title: PROMIS de-identified dataset

description: Research investigating the association of patient-reported PROMIS physical function scores and outcomes in noncardiac surgery.
DOI: 10.60593/ur.d.29636924.v1
title: PROMIS de-identified dataset

description: Research investigating the association of patient-reported PROMIS physical function scores and outcomes in noncardiac surgery.
DOI: 10.17876/musewide/dr.1/18322
title: Object GUO_ID 18322 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.29998384
title: Groundwater Automated Chemical Examiner (GrACE) System formatted data and code

description: This dataset and codebase supports the upcoming article: "A Low-Cost, Automated, and Adaptable System for Field-Based Measurement of pCO2, pCH4, and Associated Variables in Groundwater."For more information on the project, data, code, and folder structure, please refer to the readmes in the attached dataset: readme_main.docx and readme_GrACE_TH_correction.docx<br>
DOI: 10.60593/ur.d.29998384.v1
title: Groundwater Automated Chemical Examiner (GrACE) System formatted data and code

description: This dataset and codebase supports the upcoming article: "A Low-Cost, Automated, and Adaptable System for Field-Based Measurement of pCO2, pCH4, and Associated Variables in Groundwater."For more information on the project, data, code, and folder structure, please refer to the readmes in the attached dataset: readme_main.docx and readme_GrACE_TH_correction.docx<br>
DOI: 10.17876/musewide/dr.1/18324
title: Object GUO_ID 18324 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18325
title: Object GUO_ID 18325 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.30043615
title: Cell Reports Methods Paper Codes

description: This repository contains the R code used to systematically benchmark ten Seurat-implemented differential gene-expression (DGE) methods for scRNA-seq data and to provide guidance on selecting appropriate DGE methods.
DOI: 10.60593/ur.d.30043615.v1
title: Cell Reports Methods Paper Codes

description: This repository contains the R code used to systematically benchmark ten Seurat-implemented differential gene-expression (DGE) methods for scRNA-seq data and to provide guidance on selecting appropriate DGE methods.
DOI: 10.60593/ur.d.30064042
title: [ ingredients by cuisine data vizualization for Data Bloom 2024 ]

description: This visualization is a dashboard (2 pages), but the focus is on page 1, where there are 3 visuals, a word cloud, bubble chart and a bar chart, that are completely interactive (like every single visual can be interacted with to change the entire dashboard), along with selectable filters, you can use these to see real time, correlations between ingredients and cuisines, and visualize what cuisine leans towards what kind of ingredients, and even variants of specific ingredients. The second page contains, filters that show you more numerical data, where you can see side by side comparisons, of ingredients within two separate cuisines, or even the extent to which, two cuisines can use the same ingredient.This viz was submitted as part of the Data Bloom 2024 Viz competition.This viz was created using PowerBI and is based on the following data source: Kaggle - https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset/dataPowerBI or a free viewer is required to render and view the full dynamic visualization within the PBIX file.
DOI: 10.60593/ur.d.30064042.v2
title: [ ingredients by cuisine data vizualization for Data Bloom 2024 ]

description: This visualization is a dashboard (2 pages), but the focus is on page 1, where there are 3 visuals, a word cloud, bubble chart and a bar chart, that are completely interactive (like every single visual can be interacted with to change the entire dashboard), along with selectable filters, you can use these to see real time, correlations between ingredients and cuisines, and visualize what cuisine leans towards what kind of ingredients, and even variants of specific ingredients. The second page contains, filters that show you more numerical data, where you can see side by side comparisons, of ingredients within two separate cuisines, or even the extent to which, two cuisines can use the same ingredient.This viz was submitted as part of the Data Bloom 2024 Viz competition.This viz was created using PowerBI and is based on the following data source: Kaggle - https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset/dataPowerBI or a free viewer is required to render and view the full dynamic visualization within the PBIX file.
DOI: 10.17876/musewide/dr.1/18328
title: Object GUO_ID 18328 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.60593/ur.d.30064042.v1
title: [ ingredients by cuisine data viz for Data Bloom 2024 ]

description: This visualization is a dashboard (2 pages), but the focus is on page 1, where there are 3 visuals, a word cloud, bubble chart and a bar chart, that are completely interactive (like every single visual can be interacted with to change the entire dashboard), along with selectable filters, you can use these to see real time, correlations between ingredients and cuisines, and visualize what cuisine leans towards what kind of ingredients, and even variants of specific ingredients. The second page contains, filters that show you more numerical data, where you can see side by side comparisons, of ingredients within two separate cuisines, or even the extent to which, two cuisines can use the same ingredient.This viz was submitted as part of the Data Bloom 2024 Viz competition.This viz was created using PowerBI and is based on the following data source: Kaggle - https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset/dataPowerBI or a free viewer is required to render and view the full dynamic visualization within the PBIX file.
DOI: 10.17876/musewide/dr.1/18329
title: Object GUO_ID 18329 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.34740/kaggle/dsv/13091193
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17876/musewide/dr.1/18337
title: Object GUO_ID 18337 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.veyg9d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Venus subrosalina",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011847-250914085247600/datasets/export for details.
DOI: 10.17876/musewide/dr.1/18340
title: Object GUO_ID 18340 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18342
title: Object GUO_ID 18342 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18345
title: Object GUO_ID 18345 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18348
title: Object GUO_ID 18348 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18349
title: Object GUO_ID 18349 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18351
title: Object GUO_ID 18351 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18352
title: Object GUO_ID 18352 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18353
title: Object GUO_ID 18353 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18357
title: Object GUO_ID 18357 in candels-cdfs-37

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18362
title: Object GUO_ID 18362 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18364
title: Object GUO_ID 18364 in candels-cdfs-40

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/18367
title: Object GUO_ID 18367 in candels-cdfs-32

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.18130/v3/k0buxj
title: University of Virginia Amphitheater - Charlottesville - Virginia - 3D Dataset

description: 3D data record for the University of Virginia Amphitheater; data was recorded in collaboration with UVA Facilities Management mainly to document original material that was exposed in the eastern stage building during restoration in 2019; point of contact with UVA FM was Mark Kutney; because focus was just on the east building, the dataset does not completely cover all of the amphitheater site in detail;
description: data was collected with (2) X130 FARO Focus 3D terrestrial LIDAR scanners; data processed with FARO Scene 2023.0.1;
DOI: 10.15468/dl.8rayjp
title: Occurrence Download

description: A dataset containing 1087827 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apis mellifera"
 ]
}

 The dataset includes 1087827 records from 1759 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011782-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1kljfy
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005604-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12778665
title: VETO: Vessel  topology manual annotation dataset

description: We have labeled the vessel topology on four public-available retinal datasets:

INSPIRE [1]VICAVR [2]IOSTAR [3]DRIVE [4]Two experts were asked to manually label the topological information of the retinal vascular structure by using a graph editing software we developed for this task. Expert one and two independently labeled each vessel segment or centerline for all datasets, based on the types of available manual annotations of the vessel structure. Then the consensus between them was released for public use.

It is worth noting that the vessel segments or vessel centerlines were used for topology estimation were extracted either by human grader or automatic vessel segmentation method, i.e. the DRIVE and IOSTAR datasets include the manual annotations of retinal vessel for each image, so the topology reconstruction were performed at manual annotated vessel patterns; for VICAVR datasets, the topology estimation were performed at the automatic segmented vessels by using the automated segmentation method [5]; for INSPIRE dataset, the human expert graded the topology at the vessel centerline which were provided by [6].

[1] M. Niemeijer, X. Xu, A. Dumitrescu, B. van Ginneken, J. Folk, and M. Abràmoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med.Imaging, vol. 30, no. 11, pp. 1941–1950, 2011.

[2] S. G. Vázquez, B. Cancela, N. Barreira, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl., vol. 24, no. 5, pp. 919– 930, 2013.

[3] J. Zhang, B. Dashtbozorg, E. J. Bekkers, J. P. W. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging, vol. 35, pp. 2631–2644, 2016.

[4] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, pp. 501–509, 2004.

[5] Y. Zhao, L. Rada, K. Chen, , and Y. Zheng, “Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images,” IEEE Trans. Med. Imaging, vol. 34, no. 9, pp. 1797–1807, 2015.

[6] R. Estrada, C. Tomasi, S. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1688–1701, 2015.
DOI: 10.5281/zenodo.12778666
title: VETO: Vessel  topology manual annotation dataset

description: We have labeled the vessel topology on four public-available retinal datasets:

INSPIRE [1]VICAVR [2]IOSTAR [3]DRIVE [4]Two experts were asked to manually label the topological information of the retinal vascular structure by using a graph editing software we developed for this task. Expert one and two independently labeled each vessel segment or centerline for all datasets, based on the types of available manual annotations of the vessel structure. Then the consensus between them was released for public use.

It is worth noting that the vessel segments or vessel centerlines were used for topology estimation were extracted either by human grader or automatic vessel segmentation method, i.e. the DRIVE and IOSTAR datasets include the manual annotations of retinal vessel for each image, so the topology reconstruction were performed at manual annotated vessel patterns; for VICAVR datasets, the topology estimation were performed at the automatic segmented vessels by using the automated segmentation method [5]; for INSPIRE dataset, the human expert graded the topology at the vessel centerline which were provided by [6].

[1] M. Niemeijer, X. Xu, A. Dumitrescu, B. van Ginneken, J. Folk, and M. Abràmoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med.Imaging, vol. 30, no. 11, pp. 1941–1950, 2011.

[2] S. G. Vázquez, B. Cancela, N. Barreira, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl., vol. 24, no. 5, pp. 919– 930, 2013.

[3] J. Zhang, B. Dashtbozorg, E. J. Bekkers, J. P. W. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging, vol. 35, pp. 2631–2644, 2016.

[4] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, pp. 501–509, 2004.

[5] Y. Zhao, L. Rada, K. Chen, , and Y. Zheng, “Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images,” IEEE Trans. Med. Imaging, vol. 34, no. 9, pp. 1797–1807, 2015.

[6] R. Estrada, C. Tomasi, S. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1688–1701, 2015.
DOI: 10.5281/zenodo.11200353
title: CROPGO simulation files
DOI: 10.5281/zenodo.12814218
title: CSM-CROPGO-Hemp simulation files for DSSAT 4.8.2
DOI: 10.5281/zenodo.11200352
title: CSM-CROPGO-Hemp simulation files for DSSAT 4.8.2
DOI: 10.5281/zenodo.11200602
title: CROPGO simulation files
DOI: 10.15468/ldzolb
title: First record of Tettigettalna mariae Quartau & Boulard, 1995 (Insecta: Hemiptera: Cicadoidea) in Spain

description: Tettigettalna mariae Quartau & Boulard 1995 is recorded for the first time in Spain. Thought to be endemic to Portugal (occurring in the southern province of Algarve), the present paper adds its distribution to southern Spain, being an Iberian endemism. The acoustic signals of the new specimens collected were recorded in different localities of Huelva province, in Andalusia during August 2012. According to their present known distribution, specimens of T. mariae tend to be sparsely distributed in small range populations in southern Iberian Peninsula, favouring wooded areas with Pinus pinea.
DOI: 10.15468/mvhy5w
title: Coimbra Collection of Algae
DOI: 10.15468/dl.hhpcjl
title: Occurrence Download

description: A dataset containing 731 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 4c1a7622-ff80-419d-b1ba-2037d61f1973",
 "TaxonKey is Decapoda"
 ]
}

 The dataset includes 731 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005617-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lgnqhx
title: Occurrence Download

description: A dataset containing 16675294 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Spain",
 "Country is Mexico"
 ]
 },
 "Year 2010-2019"
 ]
}

 The dataset includes 16675294 records from 779 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005598-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.