cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17676219
title: Dataset for "Generation of Radially or Azimuthally Polarized Laser Beams in a Yb:YAG Thin-disc Laser"

description: This dataset includes relevant data and figures associated with the manuscript "Generation of Radially or Azimuthally Polarized Laser Beams in a Yb:YAG Thin-disc Laser".
DOI: 10.5281/zenodo.17676220
title: Dataset for "Generation of Radially or Azimuthally Polarized Laser Beams in a Yb:YAG Thin-disc Laser"

description: This dataset includes relevant data and figures associated with the manuscript "Generation of Radially or Azimuthally Polarized Laser Beams in a Yb:YAG Thin-disc Laser".
DOI: 10.5281/zenodo.15758194
title: S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects

description: Collated lists of target and suspect chemicals identified in the NORMAN Collaborative Trial (Schymanski et al. 2015., DOI: 10.1007/s00216-015-8681-7). Dataset DOI: 10.5281/zenodo.2621478.

Upload 22/3/2020: added merged InChIKey file for PubChem data extraction. 27/6/2025: added merged CSV
DOI: 10.5281/zenodo.2621478
title: S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects

description: Collated lists of target and suspect chemicals identified in the NORMAN Collaborative Trial (Schymanski et al. 2015., DOI: 10.1007/s00216-015-8681-7). Dataset DOI: 10.5281/zenodo.2621478.

Upload 22/3/2020: added merged InChIKey file for PubChem data extraction. 27/6/2025: added merged CSV
DOI: 10.15468/dl.qcr7p7
title: Occurrence Download

description: A dataset containing 28724 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-97.36242 21.60121,-97.17651 22.11883,-97.48636 23.09136,-98.46548 23.03435,-99.15953 23.09136,-98.72575 22.86315,-98.2176 21.39364,-97.36242 21.60121))",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 28724 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003631-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17683866
title: Murine BCL11B tracks
DOI: 10.5281/zenodo.17683867
title: Murine BCL11B tracks
DOI: 10.15468/dl.2n7smr
title: Occurrence Download

description: A dataset containing 8864 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "GadmGid is one of (COL.2_2)",
 "StateProvince is one of (Antioquia)",
 "Geometry POLYGON((-75.57003 6.42244,-75.56937 6.32084,-75.57003 6.2835,-75.57035 6.24454,-75.41945 6.24421,-75.41618 6.42147,-75.57003 6.42244))",
 "Issue is one of (Geodetic datum assumed WGS84, Coordinate reprojected, Country derived from coordinates)"
 ]
 }
 ]
}

 The dataset includes 8864 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003632-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9nec4e
title: Occurrence Download

description: A dataset containing 11021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-82.91625 42.42135,-82.95128 42.43581,-82.94053 42.45037,-83.28773 42.44268,-83.28638 42.407,-83.27586 42.40728,-83.27491 42.3786,-83.26658 42.37879,-83.26384 42.34169,-83.23777 42.34309,-83.23749 42.33574,-83.23503 42.33583,-83.23457 42.32984,-83.22783 42.3326,-83.22511 42.3288,-83.21528 42.32902,-83.21534 42.33614,-83.19606 42.33644,-83.19653 42.35089,-83.14766 42.35192,-83.14994 42.33984,-83.1569 42.33696,-83.15647 42.32724,-83.15313 42.32809,-83.1407 42.31101,-83.13964 42.30866,-83.14242 42.30569,-83.14045 42.29775,-83.14256 42.29441,-83.14724 42.29257,-83.15169 42.29626,-83.15769 42.29496,-83.15825 42.29202,-83.167 42.28957,-83.15816 42.27884,-83.16983 42.26458,-83.16097 42.25496,-83.13745 42.28279,-83.11877 42.27913,-83.11764 42.28041,-83.11988 42.28755,-83.11813 42.28973,-83.11463 42.29018,-83.0999 42.2867,-83.07994 42.30717,-83.06356 42.31686,-83.01894 42.33062,-82.98912 42.33248,-82.95977 42.33977,-82.94541 42.3474,-82.92397 42.35212,-82.9468 42.38699,-82.92149 42.39546,-82.91055 42.41719,-82.91034 42.41902,-82.91625 42.42135),(-83.07376 42.39872,-83.10205 42.38826,-83.12185 42.41748,-83.08947 42.41809,-83.07632 42.40185,-83.05699 42.40898,-83.05394 42.40444,-83.04245 42.40444,-83.04222 42.39021,-83.04428 42.39018,-83.04041 42.38476,-83.05467 42.3796,-83.06241 42.39124,-83.06655 42.38971,-83.07376 42.39872))"
 ]
}

 The dataset includes 11021 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003633-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240543
title: HIFLD OPEN Pipeline Areas (USACE IENC)

description: An area containing one or more pipelines. The USACE IENCs coverage area consists of 7,260 miles across 21 rivers primarily located in the Central United States. IENCs apply to inland waterways that are maintained for navigation by USACE for shallow-draft vessels (e.g., maintained at a depth of 9-14 feet, dependent upon the waterway project authorization). Generally, IENCs are produced for those commercially navigable waterways which the National Oceanic and Atmospheric Administration (NOAA) does not produce Electronic Navigational Charts (ENCs). However, Special Purpose IENCs may be produced in agreement with NOAA. Users should be aware that the IENCs reflect best available information at the time the data was collected.<br>
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e240543v1
title: HIFLD OPEN Pipeline Areas (USACE IENC)

description: An area containing one or more pipelines. The USACE IENCs coverage area consists of 7,260 miles across 21 rivers primarily located in the Central United States. IENCs apply to inland waterways that are maintained for navigation by USACE for shallow-draft vessels (e.g., maintained at a depth of 9-14 feet, dependent upon the waterway project authorization). Generally, IENCs are produced for those commercially navigable waterways which the National Oceanic and Atmospheric Administration (NOAA) does not produce Electronic Navigational Charts (ENCs). However, Special Purpose IENCs may be produced in agreement with NOAA. Users should be aware that the IENCs reflect best available information at the time the data was collected.<br>
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.5281/zenodo.2621916
title: S4 | UJIBADE | University of Jaume I Bade et al List

description: List of target substances from University Jaume I, used in Bade et al 2015, Sci. Tot. Environ. 538: 934-941. DOI: 10.1016/j.scitotenv.2015.08.078. Dataset DOI: 10.5281/zenodo.2621916. 
description: Further authorship details in the accompanying publication: https://doi.org/10.1016/j.scitotenv.2015.08.078
DOI: 10.5281/zenodo.2621917
title: S4 | UJIBADE | University of Jaume I Bade et al List

description: List of target substances from University Jaume I, used in Bade et al 2015, Sci. Tot. Environ. 538: 934-941. DOI: 10.1016/j.scitotenv.2015.08.078. Dataset DOI: 10.5281/zenodo.2621916. 
description: Further authorship details in the accompanying publication: https://doi.org/10.1016/j.scitotenv.2015.08.078
DOI: 10.5281/zenodo.2621941
title: S5 | KWRSJERPS | KWR Drinking Water Suspect List

description: KWR Drinking Water Suspect List from Sjerps et al. 2016 Water Research 93: 254-264, DOI: 10.1016/j.watres.2016.02.034. Dataset DOI: 10.5281/zenodo.2621941.
description: Further author information in this article: DOI: 10.1016/j.watres.2016.02.034
DOI: 10.5281/zenodo.2621942
title: S5 | KWRSJERPS | KWR Drinking Water Suspect List

description: KWR Drinking Water Suspect List from Sjerps et al. 2016 Water Research 93: 254-264, DOI: 10.1016/j.watres.2016.02.034. Dataset DOI: 10.5281/zenodo.2621941.
description: Further author information in this article: DOI: 10.1016/j.watres.2016.02.034
DOI: 10.15468/dl.743w3y
title: Occurrence Download

description: A dataset containing 2704 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vipera seoanei"
 ]
}

 The dataset includes 2704 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003634-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13832368
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.sqmmxb
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Minyobates bombetes"
 ]
}

 The dataset includes 17 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003636-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240544
title: HIFLD OPEN Tectonic Plate Boundaries

description: USGS Tectonic Plate Boundaries.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.<br>
DOI: 10.3886/e240544v1
title: HIFLD OPEN Tectonic Plate Boundaries

description: USGS Tectonic Plate Boundaries.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.<br>
DOI: 10.15468/dl.mf7rqr
title: Occurrence Download

description: A dataset containing 286 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "NetworkKey is 99d66b6c-9087-452f-a9d4-f15f2c2d0e7e",
 "TaxonKey is Chordata"
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Diamantomyidae [GBIF Backbone Taxonomy], Thryonomyidae [GBIF Backbone Taxonomy], Petromuridae [GBIF Backbone Taxonomy], Phiomyidae [GBIF Backbone Taxonomy])",
 "BasisOfRecord is one of (Fossil)"
 ]
 }
 ]
}

 The dataset includes 286 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003637-251120083545085/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.30687080
title: Research Data: The different osteogenic profile of miRNA obtained from Processed Lipoaspirate Cells and Dental Pulp Stem Cells.

description: Selecting the optimal mesenchymal stem cell (MSC) source for tissue engineering depends on understanding their molecular mechanisms and signaling pathways.  MicroRNAs regulate gene expression serving as markers of MSC osteogenic differentiation.  We compared dental pulp stem cells (DPSCs) and processed lipoaspirate cells (PLAs) for osteogenic potential and microRNA expression during osteogenic induction. DPSCs exhibited superior extracellular matrix production than PLAs.  Ten microRNAs were differentially expressed between DPSCs and PLAs at day 21 (miR-615-3p, miR-10b-5p, miR-10a-5p, miR-196a-5p, miR-204-5p, miR-335-5p, miR-483-3p, miR-95-3p, miR-31-5p, miR-598-3p), they could predict biomarkers for MSC osteogenic differentiation. Our findings offer insights into the molecular mechanisms underlying MSC osteogenic plasticity suggest that modulating specific microRNAs may enhance their osteogenic potential, advancing MSCs application in translational bone tissue engineering. <b>Significance Statement</b>:  Our investigation demonstrates that Dental Pulp Stem Cells (DPSCs) exhibit a statistically significant superior osteogenic differentiation potential compared to processed lipoaspirate cells (PLAs) when subjected to identical osteoinductive conditions. Furthermore, we identified ten distinct microRNAs that are consistently and differentially expressed in DPSCs. These specific microRNA profiles serve as predictive biomarkers for mesenchymal stem cell (MSC) osteogenic capacity. These findings not only elucidate the molecular underpinnings of enhanced DPSC differentiation but also offer novel, targeted strategies for modulating MSC osteogenesis, thereby significantly advancing their application in translational bone tissue engineering for complex skeletal repair challenges. <br>
DOI: 10.6084/m9.figshare.30687080.v1
title: Research Data: The different osteogenic profile of miRNA obtained from Processed Lipoaspirate Cells and Dental Pulp Stem Cells.

description: Selecting the optimal mesenchymal stem cell (MSC) source for tissue engineering depends on understanding their molecular mechanisms and signaling pathways.  MicroRNAs regulate gene expression serving as markers of MSC osteogenic differentiation.  We compared dental pulp stem cells (DPSCs) and processed lipoaspirate cells (PLAs) for osteogenic potential and microRNA expression during osteogenic induction. DPSCs exhibited superior extracellular matrix production than PLAs.  Ten microRNAs were differentially expressed between DPSCs and PLAs at day 21 (miR-615-3p, miR-10b-5p, miR-10a-5p, miR-196a-5p, miR-204-5p, miR-335-5p, miR-483-3p, miR-95-3p, miR-31-5p, miR-598-3p), they could predict biomarkers for MSC osteogenic differentiation. Our findings offer insights into the molecular mechanisms underlying MSC osteogenic plasticity suggest that modulating specific microRNAs may enhance their osteogenic potential, advancing MSCs application in translational bone tissue engineering. <b>Significance Statement</b>:  Our investigation demonstrates that Dental Pulp Stem Cells (DPSCs) exhibit a statistically significant superior osteogenic differentiation potential compared to processed lipoaspirate cells (PLAs) when subjected to identical osteoinductive conditions. Furthermore, we identified ten distinct microRNAs that are consistently and differentially expressed in DPSCs. These specific microRNA profiles serve as predictive biomarkers for mesenchymal stem cell (MSC) osteogenic capacity. These findings not only elucidate the molecular underpinnings of enhanced DPSC differentiation but also offer novel, targeted strategies for modulating MSC osteogenesis, thereby significantly advancing their application in translational bone tissue engineering for complex skeletal repair challenges. <br>
DOI: 10.6084/m9.figshare.30687080.v2
title: Research Data: The different osteogenic profile of miRNA obtained from Processed Lipoaspirate Cells and Dental Pulp Stem Cells.

description: Selecting the optimal mesenchymal stem cell (MSC) source for tissue engineering depends on understanding their molecular mechanisms and signaling pathways.  MicroRNAs regulate gene expression serving as markers of MSC osteogenic differentiation.  We compared dental pulp stem cells (DPSCs) and processed lipoaspirate cells (PLAs) for osteogenic potential and microRNA expression during osteogenic induction. DPSCs exhibited superior extracellular matrix production than PLAs.  Ten microRNAs were differentially expressed between DPSCs and PLAs at day 21 (miR-615-3p, miR-10b-5p, miR-10a-5p, miR-196a-5p, miR-204-5p, miR-335-5p, miR-483-3p, miR-95-3p, miR-31-5p, miR-598-3p), they could predict biomarkers for MSC osteogenic differentiation. Our findings offer insights into the molecular mechanisms underlying MSC osteogenic plasticity suggest that modulating specific microRNAs may enhance their osteogenic potential, advancing MSCs application in translational bone tissue engineering. <b>Significance Statement</b>:  Our investigation demonstrates that Dental Pulp Stem Cells (DPSCs) exhibit a statistically significant superior osteogenic differentiation potential compared to processed lipoaspirate cells (PLAs) when subjected to identical osteoinductive conditions. Furthermore, we identified ten distinct microRNAs that are consistently and differentially expressed in DPSCs. These specific microRNA profiles serve as predictive biomarkers for mesenchymal stem cell (MSC) osteogenic capacity. These findings not only elucidate the molecular underpinnings of enhanced DPSC differentiation but also offer novel, targeted strategies for modulating MSC osteogenesis, thereby significantly advancing their application in translational bone tissue engineering for complex skeletal repair challenges. <br>
DOI: 10.3886/icpsr39580
title: Matching Complex Patients to Treatments: Innovative Statistical Scoring Methods for Treatment Selection [Methods Study], 2015-2020
title: Archival Version

description: Patients may respond differently to the same treatment due to differences in personal traits such as age, gender, or the number and type of health problems they have. Researchers use statistical methods to predict how well a treatment may work for patients based on their personal traits. But current methods may not work well if patients have many health problems or are taking other medicines.
In this project, the research team created new methods to figure out which patient traits are related to treatment benefits to help doctors and patients understand the likely treatment benefits for individual patients.
 To access the methods, software, and R package, please visit the personalized
CRAN webpage and personalized GitHub
description: To develop and test methods for estimating HTE for different patient characteristics to rank treatment benefits for patients. Researchers had 3 specific aims: aim 1 was to develop treatment scoring systems
using existing single clinical trial data sets with outcomes collected longitudinally. Aim 2 was to develop treatment scoring systems using existing data sets from multiple clinical trials. Aim 3 was to develop treatment scoring systems using existing data sets from large-scale observational studies.
description: To measure HTE, researchers estimated the treatment benefit for individual patients, also known as the individual treatment effect (ITE), based on different patient characteristics.
First, researchers developed a general statistical framework for estimating ITE to identify patient characteristics that moderate treatment outcomes. Using different estimation methods, researchers modeled treatment assignment rather than treatment outcomes. They included outcomes as weights to examine the effect of interactions between treatments and patient characteristics on various treatment assignments.
Using simulation studies, researchers evaluated the framework and tested the performance of different estimation methods. Researchers then used the ITE estimates to create treatment benefit scores. The scores indicated a treatment's benefit based on the influence of patient characteristics. Researchers used the scores to rank treatment benefits for individual patients based on their specific patient characteristics.
Researchers tested the methods using data from an observational study to identify which patient characteristics were related to benefits from a chronic care management intervention. They adapted the methods for three separate longitudinal outcomes.
Researchers worked with members of a health system management team, a patient, a caregiver, patient advocates, biostatisticians, and clinician researchers.
description: Datasets: 
DS1: Matching Complex Patients to Treatments: Innovative Statistical Scoring Methods for Treatment Selection [Methods Study], 2015-2020
DOI: 10.3886/icpsr39580.v1
title: Matching Complex Patients to Treatments: Innovative Statistical Scoring Methods for Treatment Selection [Methods Study], 2015-2020
title: Version 1

description: Patients may respond differently to the same treatment due to differences in personal traits such as age, gender, or the number and type of health problems they have. Researchers use statistical methods to predict how well a treatment may work for patients based on their personal traits. But current methods may not work well if patients have many health problems or are taking other medicines.
In this project, the research team created new methods to figure out which patient traits are related to treatment benefits to help doctors and patients understand the likely treatment benefits for individual patients.
 To access the methods, software, and R package, please visit the personalized
CRAN webpage and personalized GitHub
description: To develop and test methods for estimating HTE for different patient characteristics to rank treatment benefits for patients. Researchers had 3 specific aims: aim 1 was to develop treatment scoring systems
using existing single clinical trial data sets with outcomes collected longitudinally. Aim 2 was to develop treatment scoring systems using existing data sets from multiple clinical trials. Aim 3 was to develop treatment scoring systems using existing data sets from large-scale observational studies.
description: To measure HTE, researchers estimated the treatment benefit for individual patients, also known as the individual treatment effect (ITE), based on different patient characteristics.
First, researchers developed a general statistical framework for estimating ITE to identify patient characteristics that moderate treatment outcomes. Using different estimation methods, researchers modeled treatment assignment rather than treatment outcomes. They included outcomes as weights to examine the effect of interactions between treatments and patient characteristics on various treatment assignments.
Using simulation studies, researchers evaluated the framework and tested the performance of different estimation methods. Researchers then used the ITE estimates to create treatment benefit scores. The scores indicated a treatment's benefit based on the influence of patient characteristics. Researchers used the scores to rank treatment benefits for individual patients based on their specific patient characteristics.
Researchers tested the methods using data from an observational study to identify which patient characteristics were related to benefits from a chronic care management intervention. They adapted the methods for three separate longitudinal outcomes.
Researchers worked with members of a health system management team, a patient, a caregiver, patient advocates, biostatisticians, and clinician researchers.
description: Datasets: 
DS1: Matching Complex Patients to Treatments: Innovative Statistical Scoring Methods for Treatment Selection [Methods Study], 2015-2020