cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.uwzxme
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-114.1188 30.59913, -113.9688 30.59913, -113.9688 30.74913, -114.1188 30.74913, -114.1188 30.59913))"
 ]
}

 The dataset includes 32 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037145-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fsqp36
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.9688 30.59913, -113.8188 30.59913, -113.8188 30.74913, -113.9688 30.74913, -113.9688 30.59913))"
 ]
}

 The dataset includes 91 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037146-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cujnd9
title: Occurrence Download

description: A dataset containing 1901 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Catálogo de especies registradas en el Herbario ICAI"
 ]
}

 The dataset includes 1901 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010978-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r668qq
title: Occurrence Download

description: A dataset containing 139634 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of New Mexico Herbarium"
 ]
}

 The dataset includes 139634 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002126-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.12757/bbk.austacon.0224.01.01
title: External Position of Banks - consolidated banks
DOI: 10.15468/dl.6a2qhs
title: Occurrence Download

description: A dataset containing 190 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 COUNT(*) occurrences
FROM
 occurrence
WHERE
 occurrence.decimallatitude &gt;= 36.87114
 AND occurrence.decimallatitude &lt;= 36.93595
 AND (occurrence.decimallongitude &gt;= -4.90909 AND occurrence.decimallongitude &lt;= -4.79128)
 AND GBIF_WITHIN('POLYGON ((-4.90909 36.88735, -4.88015 36.87159, -4.83857 36.87114, -4.79128 36.91576, -4.80858 36.93595, -4.83757 36.93595, -4.86375 36.93146, -4.90909 36.88735))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 212 OR occurrence.acceptedtaxonkey = 212 OR occurrence.kingdomkey = 212 OR occurrence.phylumkey = 212 OR occurrence.classkey = 212 OR occurrence.orderkey = 212 OR occurrence.familykey = 212 OR occurrence.genuskey = 212 OR occurrence.specieskey = 212)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.specieskey IS NOT NULL
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey

 The dataset's 190 records were derived from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030854-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15412837
title: TILSHUNOSLIKDA XUSUSIY DISKURS TADQIQI
DOI: 10.5281/zenodo.15412838
title: TILSHUNOSLIKDA XUSUSIY DISKURS TADQIQI
DOI: 10.15468/dl.t9muhp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-79.51412 43.69517,-79.50967 43.69517,-79.50967 43.69845,-79.51412 43.69845,-79.51412 43.69517))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chaetura pelagica (Linnaeus, 1758)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030840-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29066399
title: CASE118 with energy cost variation

description: This is the first dataset of the NeurIPS 2025 submission: The SafePowerGraph Benchmark: Toward Reliable and Realistic Graph Learning in Power Grids.
DOI: 10.15468/dl.v5w69w
title: Occurrence Download

description: A dataset containing 27611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CAN.11.20.20_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 27611 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030844-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15412859
title: GTEs R package

description: GTE model quantifies batch effects for individual genes in single-cell data.
DOI: 10.5281/zenodo.15412860
title: GTEs R package

description: GTE model quantifies batch effects for individual genes in single-cell data.
DOI: 10.5517/ccdc.csd.cc2mbkh1
title: CCDC 2423223: Experimental Crystal Structure Determination

description: Related Article: Qinglong Qiao, Chao Wang, Hanlixin Wang, Yiyan Ruan, Wenjuan Liu, Jie Chen, Zhimin Wu, Xiaogang Liu, Zhaochao Xu|2025|J.Am.Chem.Soc.|147|15602|doi:10.1021/jacs.5c02725
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2mbkk3
title: CCDC 2423225: Experimental Crystal Structure Determination

description: Related Article: Qinglong Qiao, Chao Wang, Hanlixin Wang, Yiyan Ruan, Wenjuan Liu, Jie Chen, Zhimin Wu, Xiaogang Liu, Zhaochao Xu|2025|J.Am.Chem.Soc.|147|15602|doi:10.1021/jacs.5c02725
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2mbkl4
title: CCDC 2423226: Experimental Crystal Structure Determination

description: Related Article: Qinglong Qiao, Chao Wang, Hanlixin Wang, Yiyan Ruan, Wenjuan Liu, Jie Chen, Zhimin Wu, Xiaogang Liu, Zhaochao Xu|2025|J.Am.Chem.Soc.|147|15602|doi:10.1021/jacs.5c02725
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.15412870
title: METHODS FOR INCREASING MOTIVATION AND LEARNING EFFICIENCY THROUGH PEDAGOGICAL APPROACHES IN PHYSICAL EDUCATION CLASSES
DOI: 10.5281/zenodo.15412871
title: METHODS FOR INCREASING MOTIVATION AND LEARNING EFFICIENCY THROUGH PEDAGOGICAL APPROACHES IN PHYSICAL EDUCATION CLASSES
DOI: 10.15468/dl.cebwfx
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is eButterfly Surveys",
 "GadmGid is CAN.11.20.20_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030845-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rvdn9j
title: Occurrence Download

description: A dataset containing 109474629 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Observation.org, Nature data from around the World"
 ]
}

 The dataset includes 109474629 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030859-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10616433
title: Ceilometer measurements dataset

description: This dataset contains images obtained by appropriate transformation of cloud detections from a ceilometer located in San Giovanni La Punta (Catania, Italy), from January 2023 to mid-March 2023. We used a Lufft CHM 15k ceilometer that leverages Light Detection and Ranging (LiDAR) technology. The ceilometer took measurements every 15 seconds quantifying the concentration of particles in the atmosphere. By taking advantage of the reflection, it is possible to determine the cloud layer coverage. Once raw data was collected, the variables of interest were scaled using a normalisation factor of the lidar-based ceilometer. All the variables are used to generate backscatter profiles. Specifically, we plot the time on the x-axis and the height of the measured particles (contained in the backscatter coefficient) on the y-axis. The colour of the plot depends on the intensity of the measured particle: intense blue means absence of particulate; red means intense presence of particulate. Numerically, the scale goes from the value 0 to the value of 5 · 10−6. We generated a backscatter profile for each day of measurements and each of these were further divided for every hour. In this way, we obtained 24 backscatter profiles for each day of observation. 

The file uploaded is a file with '. ZIP' extension. Once extracted, there are two folders: train and test. For each folder, there are two classes of images: true and false. We suggest using 'ImageFolder' to use the dataset (see reference: https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html)

For an example of using the dataset, please visit the following authors' repository: https://github.com/alessiochisari/CeilometerDatasetBenchmark

The repository contains all the Jupyter Notebooks used by the authors to benchmark five state-of-the-art models on this new dataset.

Please cite our work for any use of the dataset or code in this repository using this citation:

@inproceedings{chisari2024, author = {Alessio Barbaro Chisari and others}, title = {On the Cloud Detection from Backscattered Images Generated From a Lidar-Based Ceilometer: Current State and Opportunities}, booktitle = {{IEEE} {ICIP} 2024, Abu Dhabi, UAE, October 27-30, 2024}, year = {2024} }
DOI: 10.5281/zenodo.10616434
title: Ceilometer measurements dataset

description: This dataset contains images obtained by appropriate transformation of cloud detections from a ceilometer located in San Giovanni La Punta (Catania, Italy), from January 2023 to mid-March 2023. We used a Lufft CHM 15k ceilometer that leverages Light Detection and Ranging (LiDAR) technology. The ceilometer took measurements every 15 seconds quantifying the concentration of particles in the atmosphere. By taking advantage of the reflection, it is possible to determine the cloud layer coverage. Once raw data was collected, the variables of interest were scaled using a normalisation factor of the lidar-based ceilometer. All the variables are used to generate backscatter profiles. Specifically, we plot the time on the x-axis and the height of the measured particles (contained in the backscatter coefficient) on the y-axis. The colour of the plot depends on the intensity of the measured particle: intense blue means absence of particulate; red means intense presence of particulate. Numerically, the scale goes from the value 0 to the value of 5 · 10−6. We generated a backscatter profile for each day of measurements and each of these were further divided for every hour. In this way, we obtained 24 backscatter profiles for each day of observation. 

The file uploaded is a file with '. ZIP' extension. Once extracted, there are two folders: train and test. For each folder, there are two classes of images: true and false. We suggest using 'ImageFolder' to use the dataset (see reference: https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html)

For an example of using the dataset, please visit the following authors' repository: https://github.com/alessiochisari/CeilometerDatasetBenchmark

The repository contains all the Jupyter Notebooks used by the authors to benchmark five state-of-the-art models on this new dataset.

Please cite our work for any use of the dataset or code in this repository using this citation:

@inproceedings{chisari2024, author = {Alessio Barbaro Chisari and others}, title = {On the Cloud Detection from Backscattered Images Generated From a Lidar-Based Ceilometer: Current State and Opportunities}, booktitle = {{IEEE} {ICIP} 2024, Abu Dhabi, UAE, October 27-30, 2024}, year = {2024} }
DOI: 10.5281/zenodo.15412730
title: Data Availability Statement for Project Management of Brownfield Modifications on Fixed Offshore Platforms Structural Integrity and Compliance with API RP 2SIM

description: This file contains the Data Availability Statement for the manuscript titled ( Project Management of Brownfield Modifications on Fixed Offshore Platforms Structural Integrity and Compliance with API RP 2SIM). 

No new data were generated or analyzed in this study.
DOI: 10.5281/zenodo.15412731
title: Data Availability Statement for Project Management of Brownfield Modifications on Fixed Offshore Platforms Structural Integrity and Compliance with API RP 2SIM

description: This file contains the Data Availability Statement for the manuscript titled ( Project Management of Brownfield Modifications on Fixed Offshore Platforms Structural Integrity and Compliance with API RP 2SIM). 

No new data were generated or analyzed in this study.
DOI: 10.15468/dl.cnxg5j
title: Occurrence Download

description: A dataset containing 111 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eustenopus villosus (Boheman, 1843)"
 ]
}

 The dataset includes 111 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030846-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/40e5633c57355bba323cc25caffb32e8
title: Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Yuba River

description: California Department of Water Resources (DWR) currently operates
 multiple eight-foot rotary screw traps (RSTs) at Hallwood Boulevard
 RM 7.5. RST operations were conducted by CDFW and then under the
 direction of the Yuba River Management Team from 1999 to 2009 at the
 Hallwood Boulevard site; and resumed in 2022 by DWR. The Hallwood
 site will essentially sample all areas of the lower Yuba River
 upstream of Hallwood Boulevard. Additionally, the Hallwood site is
 located downstream of the bulk of adult Chinook salmon spawning
 areas and downstream of all known spring-run Chinook salmon
 spawning. Data are currently being used to inform the development of
 a juvenile production estimate (JPE) for spring-run Chinook salmon
 in the Sacramento River Watershed as required by Incidental Take
 Permit No. 2081-2019-006-00 issued by CA Department of Fish and
 Wildlife (CDFW) to DWR for the long-term operation of the State
 Water Project. Data within the current year’s monitoring season are
 considered provisional.
DOI: 10.15468/dl.q4sz2j
title: Occurrence Download

description: A dataset containing 4017 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jarava ichu Ruiz & Pav."
 ]
}

 The dataset includes 4017 records from 157 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030847-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15412146
title: TABLE 2 in The Complete Mitochondrial Genome of the American Palm Cixiid, Haplaxius crudus (Hemiptera: Cixiidae)

description: TABLE 2: Annotation of the mitochondrial genome of Haplaxius crudus.
GeneStrandStartEndSizeIntergenic NtStartStopAnticodontRNA-Ile+163640——GATtRNA-Gln−1296070−5——TTGtRNA-Met+12619367−3——CATnad2+19311589650ATTTAA—tRNA-Trp+1156121963−3——TCAtRNA-Cys−1269120961−11——GCAtRNA-Tyr−1329126961−1——GTAcox1+133428721578−5ATTTAA—tRNA-Leu+2867293266−6——TAAcox2+293236016690ATGTAA—tRNA-Lys+3595366773−69——CTTtRNA-Asp+3666371564−2——GTCatp8+3729382496−13ATGTAA—atp6+38234474651−1ATATAA—cox3+44765258782−2ATTTAA—tRNA-Gly+5258531962−3——TCCnad3+531956703510ATATAA—tRNA-Ala+5670572960−1——TGCtRNA-Arg+57395801639——TCGtRNA-Asn+5798586164−4——GTTtRNA-Ser+5857592670−5——GCTtRNA-Glu+59275987610——TTCtRNA-Phe−6050598764−1——GAAnad5−7717605711546ATATAC—tRNA-His−779777336515——GTGnad4−911477771679−21ATTTAA—nad4L−94559105351−10ATTTAA—tRNA- Th r+9383944462−278——TGTtRNA-Pro−9510944368−12——TGGnad6+950310042540−43ATTTAA—Cytb+10008111321125−37ATGTAG—tRNA-Ser+111311119262−52——TGAnad1−1212311143981−50ATGTAA—tRNA-Leu−1218812124650——TAGrRNA-16S−1341012670122214———tRNA-Val−134491338268−402——TACrRNA-12S−1419013445746−5———Control regionN/A147201584811290———
Protein-coding genes � italicized.
description: Published as part of Komondy, Lidia, Huguet-Tapia, Jose, Ascunce, Marina S., Helmick, Ericka E., Goss, Erica M. & Bahder, Brian W., 2021, The Complete Mitochondrial Genome of the American Palm Cixiid, Haplaxius crudus (Hemiptera: Cixiidae), pp. 5 in Psyche: A Journal of Entomology (6625462) 2021 on page 5, DOI: 10.1155/2021/6625462, http://zenodo.org/record/15391578
DOI: 10.5281/zenodo.15412148
title: TABLE 3 in The Complete Mitochondrial Genome of the American Palm Cixiid, Haplaxius crudus (Hemiptera: Cixiidae)

description: TABLE 3: Pairwise comparison of the entire mitochondrial genome of various insect taxa relative to Haplaxius crudus representing percent difference in sequence homology.
1234567891H. crudus—0.40.40.30.40.50.40.40.32N. lugens28.7—0.30.30.40.50.50.40.43A. aurantii30.732.6—0.40.30.40.50.40.34D. baccarum32.235.033.1—0.40.40.40.40.55M. tredecassini33.034.831.832.0—0.30.40.40.36S. oryzae32.734.231.431.932.0—0.40.40.47C. opacus30.733.529.831.030.927.7—0.50.48N. pygmaea32.535.031.930.931.829.729.0—0.49D. melanogaster31.033.729.029.029.026.725.624.5—
description: Published as part of Komondy, Lidia, Huguet-Tapia, Jose, Ascunce, Marina S., Helmick, Ericka E., Goss, Erica M. & Bahder, Brian W., 2021, The Complete Mitochondrial Genome of the American Palm Cixiid, Haplaxius crudus (Hemiptera: Cixiidae), pp. 7 in Psyche: A Journal of Entomology (6625462) 2021 on page 7, DOI: 10.1155/2021/6625462, http://zenodo.org/record/15391578
DOI: 10.6073/pasta/bc031efa352e8a36c3509f291aaabb7f
title: Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Yuba River

description: California Department of Water Resources (DWR) currently operates
 multiple eight-foot rotary screw traps (RSTs) at Hallwood Boulevard
 RM 7.5. RST operations were conducted by CDFW and then under the
 direction of the Yuba River Management Team from 1999 to 2009 at the
 Hallwood Boulevard site; and resumed in 2022 by DWR. The Hallwood
 site will essentially sample all areas of the lower Yuba River
 upstream of Hallwood Boulevard. Additionally, the Hallwood site is
 located downstream of the bulk of adult Chinook salmon spawning
 areas and downstream of all known spring-run Chinook salmon
 spawning. Data are currently being used to inform the development of
 a juvenile production estimate (JPE) for spring-run Chinook salmon
 in the Sacramento River Watershed as required by Incidental Take
 Permit No. 2081-2019-006-00 issued by CA Department of Fish and
 Wildlife (CDFW) to DWR for the long-term operation of the State
 Water Project. Data within the current year’s monitoring season are
 considered provisional.
DOI: 10.15468/dl.4gs4pm
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Armeria sampaioi (Bernis) Nieto Fel."
 ]
}

 The dataset includes 56 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030849-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15256349
title: Exploring Technostress and Its Impact on Employees in IT-Driven Work Performance

description: Abstract—In recent years, the implementation of ICT in various companies has become more and more popular. Although its adoption and implementation have a good impact on the company's business, it also has a negative impact on the employees, which are also known as technostress. The goal of this research is to examine the influence of technostress on employee's work performance. The total data collected consists of 446 participants who come from a different area, such as JABODETABEK Indonesia. The study is done online, using Google Forms, from April 28, 2024, to 30 November 2024. And be processed using Partial Least Squares Structural Equation Modeling (PLS-SEM), using Smart PLS 4.0 for assessing the quality of each variable by measuring data validation and reliability. Several variables will be examined such as techno-overload, techno-invasion, techno-uncertainty, technostress, productivity, quality of work life, exhaustion, job satisfaction, and work performance. This research uses non-probability purposive sampling with criteria as employees experienced challenges when implementing a new set of skills required for learning new software technologies. This research findings show that 11 out of the 11 hypotheses have significant impact.