cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/ar/nqaox8
title: PAN-00178622 - hinged collar brooch variant C

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00178622&lt;/p&gt;
description: Modified: 2025-08-18T12:09:39+02:00
DOI: 10.25430/researchdata.cab.unipd.it.00001700
title: ONLINE APPENDIX OF: PETTRACHIN, 2025 (ITALIAN JOURNAL OF PUBLIC POLICY - RIPP)

description: This is the Online Appendix of the article:
Pettrachin A. 2025. "The local strength of radical right parties and pro-migrant local elites’ responsiveness to public opinion in the refugee integration policy field. A mixed-methods study". Italian Journal of Public Policy. Online First.
DOI: 10.34740/kaggle/dsv/13961246
title: TLDR pages translation pairs dataset
DOI: 10.15468/dl.xjpjkq
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-97.03377580052326 31.860548596713794, -97.03672059736601 31.860818438973794, -97.03967048363741 31.8605926805641, -97.04251202968764 31.859880004023477, -97.04513597908756 31.858707817695425, -97.04744145603601 31.8571211993057, -97.049339847883 31.85518115888891, -97.05075821266314 31.852962288544838, -97.05164208035595 31.85054989020567, -97.05195754050753 31.84803669266088, -97.05169253684635 31.84551928474327, -97.05085732044473 31.843094402249267, -97.04948404555769 31.840855211470757, -97.04762552522585 31.83888773200144, -97.04535319579101 31.83726753581028, -97.04275436944398 31.836056848710044, -97.03992888071983 31.835302164748764, -97.0369852555305 31.835032464335853, -97.0340365491017 31.835258103838743, -97.03119601146282 31.835970418821248, -97.02857274552157 31.837142055983737, -97.0262675230287 31.83872802120595, -97.02436891789567 31.84066740389216, -97.02294990455877 31.842885712076043, -97.02206505177536 31.8452977294092, -97.02174841997041 31.847810785106947, -97.0220122437891 31.850328311931364, -97.02284645177939 31.85275355598347, -97.02421904320775 31.85499329592751, -97.02607730908389 31.856961428564325, -97.02834985180468 31.858582282486072, -97.03094932670845 31.859793531746536, -97.03377580052326 31.860548596713794))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022625-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uw5tnk
title: Occurrence Download

description: A dataset containing 8876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((152.249591 -32.433249, 152.210855 -32.396369, 152.163479 -32.396369, 152.202203 -32.433249, 152.249591 -32.433249))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 8876 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022621-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr38811.v1
title: Using Physician Behavioral Big Data for High Precision Fraud Prediction and Detection, United States, 2000-2019
title: Version 1

description: This project used big data from non-clinical physician behavior. These include traffic violations, substance abuse, property ownership, stressors (e.g., bankruptcy and divorce), social media data, and other life events data. These variables, all based on public records, were used to construct a predictive model of Medicare fraud using machine learning techniques.
description: The study team aimed to provide new policy-relevant applications using data science to improve risk assessment of physician engagement in fraud. Specifically, they sought answers to the following questions:

Can models using big data on non-clinical physician behavior (e.g., illegal behavior, consumer complaints and malpractice, other disciplinary action, conspicuous spending, and life stressors) successfully predict engagement in fraud in the near-term future (1-5 years)?
Of these behavioral factors, which ones represent the greatest risk for fraud engagement?
Which machine learning algorithm is most accurate in predicting a physician's risk of engaging in fraud?
description: Both static and dynamic variables were acquired on the two groups of physicians in this study. Dynamic variables were collected from 2000 to 2019 and include information about physician's online reviews, political donations, transaction records, criminal records, services to Medicare beneficiaries, and background checks. Static variables were collected in 2020 and include physician demographic information, practices, geolocations, and background checks.
description: The sample consisted of physicians excluded from Medicare participation between 2015 through 2019 due to fraudulent activity and a control group of matched non-fraudulent physicians. Matching was initially based on five characteristics (i.e., gender, primary practice zip code, primary taxonomy, whether the physician was the singular owner of the practice, and degree credentials). Later age (+ or - 5 years) was added as a matching criteria. To get a complete control sample, the study team loosened the matching criteria somewhat, reclassifying some taxonomies (the National Provider Identifier Registry term for medical specialties) to be slightly broader (e.g., 'Internal Medicine' instead of 'Internal Medicine Cardiovascular Disease').
description: Datasets: 
DS0: Study-Level Files
DS1: Legal Case Information Data
DS2: Social and Behavioral Background Data
description: Physicians in the United States that have been excluded from federally funded health programs and a matched sample of non-excluded physicians that share a similar location, age, gender, and credentials. Smallest Geographic Unit: Zip Code
description: web scraping
DOI: 10.7910/dvn/yvbkwt
title: Replication data for: Learning Before Testing: A Selective Nonparametric Test for Conditional Moment Restrictions

description: Review of Economics and Statistics: Forthcoming.
description: Volume XX, Issue X
DOI: 10.3886/icpsr38811
title: Using Physician Behavioral Big Data for High Precision Fraud Prediction and Detection, United States, 2000-2019
title: Archival Version

description: This project used big data from non-clinical physician behavior. These include traffic violations, substance abuse, property ownership, stressors (e.g., bankruptcy and divorce), social media data, and other life events data. These variables, all based on public records, were used to construct a predictive model of Medicare fraud using machine learning techniques.
description: The study team aimed to provide new policy-relevant applications using data science to improve risk assessment of physician engagement in fraud. Specifically, they sought answers to the following questions:

Can models using big data on non-clinical physician behavior (e.g., illegal behavior, consumer complaints and malpractice, other disciplinary action, conspicuous spending, and life stressors) successfully predict engagement in fraud in the near-term future (1-5 years)?
Of these behavioral factors, which ones represent the greatest risk for fraud engagement?
Which machine learning algorithm is most accurate in predicting a physician's risk of engaging in fraud?
description: Both static and dynamic variables were acquired on the two groups of physicians in this study. Dynamic variables were collected from 2000 to 2019 and include information about physician's online reviews, political donations, transaction records, criminal records, services to Medicare beneficiaries, and background checks. Static variables were collected in 2020 and include physician demographic information, practices, geolocations, and background checks.
description: The sample consisted of physicians excluded from Medicare participation between 2015 through 2019 due to fraudulent activity and a control group of matched non-fraudulent physicians. Matching was initially based on five characteristics (i.e., gender, primary practice zip code, primary taxonomy, whether the physician was the singular owner of the practice, and degree credentials). Later age (+ or - 5 years) was added as a matching criteria. To get a complete control sample, the study team loosened the matching criteria somewhat, reclassifying some taxonomies (the National Provider Identifier Registry term for medical specialties) to be slightly broader (e.g., 'Internal Medicine' instead of 'Internal Medicine Cardiovascular Disease').
description: Datasets: 
DS0: Study-Level Files
DS1: Legal Case Information Data
DS2: Social and Behavioral Background Data
description: Physicians in the United States that have been excluded from federally funded health programs and a matched sample of non-excluded physicians that share a similar location, age, gender, and credentials. Smallest Geographic Unit: Zip Code
description: web scraping
DOI: 10.15468/dl.64r85k
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Astragalus leptocladus"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022623-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/bm5zm2
title: PAN-00095046 - coin/coin-related, cob / reales

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00095046&lt;/p&gt;
description: Modified: 2025-09-15T13:32:57+02:00
DOI: 10.15468/dl.6p74n6
title: Occurrence Download

description: A dataset containing 4635 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Callicarpa arborea, Callicarpa bodinieri, Callicarpa brevipes, Callicarpa dentosa, Callicarpa giraldii)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4635 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022627-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x9uhct
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bellia picta",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 71 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022626-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhbgyh
title: Occurrence Download

description: A dataset containing 11474 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((151.485580 -32.829086, 151.450791 -32.796310, 151.408640 -32.796310, 151.443419 -32.829086, 151.485580 -32.829086))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 11474 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022628-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/y4hdht
title: PAN-00085010 - coin/coin-related, Commodus (180-192), voor Crispina, sestertius

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00085010&lt;/p&gt;
description: Modified: 2025-08-19T10:47:28+02:00
DOI: 10.15468/dl.ktq6p4
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Biflustra grandicella",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 49 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022629-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-z3u-e325
title: PAN-00121300 - terra sigillata plate with base plate

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00121300&lt;/p&gt;
description: Modified: 2025-09-15T09:33:47+02:00
DOI: 10.15468/dl.9jwxgg
title: Occurrence Download

description: A dataset containing 11635 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Callicarpa hungtaii, Callicarpa japonica, Callicarpa kochiana, Callicarpa longifolia, Callicarpa longipes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11635 records from 199 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022630-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xm9yzs
title: Occurrence Download

description: A dataset containing 539 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amblyomma dubitatum"
 ]
}

 The dataset includes 539 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022631-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m25ruj
title: Occurrence Download

description: A dataset containing 23337 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "GadmGid is one of (ESP.2_1, ESP.2.2_1, ESP.2.3_1, ESP.2.1_1)",
 "Geometry POLYGON((-2.07113 41.09753,-1.9758 41.10573,-1.84576 41.05046,-1.69342 40.93173,-1.68315 40.85054,-1.61312 40.79567,-1.61629 40.64257,-1.69906 40.64353,-1.74686 40.61703,-1.76051 40.51808,-1.86824 40.4291,-1.85767 40.35693,-1.78768 40.32018,-1.7888 40.25713,-1.75403 40.22973,-1.68352 40.22895,-1.55617 40.13734,-1.50903 40.14575,-1.50962 40.11873,-1.46331 40.0911,-1.29914 40.08881,-1.19434 40.06019,-1.23043 40.02471,-1.23159 39.97969,-1.19765 39.93412,-1.16304 39.91556,-0.97568 39.92144,-0.97779 39.84941,-0.92074 39.80336,-0.85098 39.79307,-0.75615 39.83632,-0.73071 39.89889,-0.77601 39.94481,-0.69232 39.99723,-0.57432 40.02184,-0.49814 40.18237,-0.3321 40.2237,-0.28154 40.3126,-0.22165 40.33819,-0.21869 40.41018,-0.2415 40.42874,-0.20506 40.45489,-0.20319 40.49988,-0.23559 40.57272,-0.23262 40.64471,-0.1728 40.66127,-0.17051 40.71526,0.06725 40.64062,0.10152 40.66851,0.20749 40.68013,0.34469 40.80046,0.31701 40.8989,0.3623 40.95399,0.36086 40.98998,0.34682 41.0437,0.28555 41.08731,0.44717 41.2171,0.4563 41.28935,0.43138 41.31582,0.4412 41.37008,0.4166 41.38754,0.41445 41.44153,0.46159 41.4606,0.51865 41.53389,0.49087 41.63236,0.41819 41.64876,0.40511 41.67548,0.45169 41.71255,0.49973 41.71361,0.53369 41.76838,0.59346 41.77865,0.67502 41.85238,0.67175 41.94238,0.73042 41.98858,0.78511 42.15175,0.77175 42.18752,0.83945 42.3329,0.78587 42.47597,0.82078 42.52166,0.82889 42.63889,0.67862 42.744,0.32542 42.71828,0.32349 42.76326,0.23723 42.77922,0.06673 42.76597,0.0558 42.73868,0 42.71922,0 42.6922,-0.04573 42.69339,-0.15176 42.77712,-0.15052 42.80411,-0.19979 42.79632,-0.2711 42.84306,-0.35667 42.84507,-0.38264 42.80965,-0.43189 42.80176,-0.51633 42.83064,-0.52999 42.79492,-0.55442 42.79544,-0.66099 42.88772,-0.72217 42.88895,-0.71985 42.95194,-0.74369 42.97042,-0.86716 42.94577,-0.91858 42.87468,-0.9207 42.81169,-0.99607 42.75,-1.05714 42.75105,-1.10763 42.69786,-1.18084 42.69905,-1.24338 42.64599,-1.24414 42.61899,-1.30557 42.6019,-1.35573 42.5486,-1.35738 42.48559,-1.40695 42.45027,-1.42159 42.35141,-1.45866 42.32489,-1.4602 42.26187,-1.48487 42.24419,-1.46413 42.09982,-1.40503 42.04497,-1.44303 41.97344,-1.55182 41.96586,-1.61117 42.01163,-1.69568 42.01262,-1.71929 42.0399,-1.81559 42.05895,-1.87601 42.05956,-1.91272 42.03289,-1.92722 41.88892,-1.89184 41.84355,-1.91729 41.76274,-1.86966 41.73526,-1.87013 41.70824,-1.95513 41.655,-2.05146 41.63782,-2.06451 41.56587,-2.04079 41.54766,-2.04172 41.48462,-2.13742 41.49439,-2.23484 41.35996,-2.21331 41.16165,-2.07113 41.09753),(-0.73855 40.01615,-0.7387 40.01167,-0.73853 40.01678,-0.73855 40.01615))"
 ]
 },
 "EventDate 2025-07-01-2025-12-02"
 ]
}

 The dataset includes 23337 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022576-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gbqgqd
title: Occurrence Download

description: A dataset containing 6477285 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-84.46739 10.11269,-84.37343 9.96372,-83.8447 9.96372,-83.71957 10.11269,-83.75693 10.7866,-84.17875 10.82007,-84.43988 10.41834,-84.46739 10.11269))"
 ]
}

 The dataset includes 6477285 records from 725 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022572-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34894/dusjvq
title: Standardized, HISCO-coded and classified occupational titles, release 2018.01

description: Here you find a release of the standardized and coded values of 134,964 different occupational titles found in the sources used by the HSN until the 1st of July 2018. 

All these occupational titles have been coded into HISCO, HISCLASS, HISCAM, OCC1950 and SOCPO classifications. The process of coding of the occupational titles followed three stages: 1) All titles were standardized into a uniform spelling, 2) The resulting standards were provided with a HISCO code and 3) On the basis of the HISCO code other classifications like HISCLASS were added to the standards.

The system will be developed continuously; in the future not only mistakes will be repaired and new titles will be added, but on the basis of HISCO other class and prestige stratifications may be included as well.

When using these data in any form (presentations, books, articles etc.) the dataset should be cited as Kees Mandemakers, Rick J. Mourits, Sanne Muurling, Corinne Boter, Ingrid K. van Dijk, Ineke Maas, Bart Van de Putte, Richard L. Zijdeman, Paul Lambert, Marco H.D. van Leeuwen, Frans van Poppel and Andrew Miles, HSN standardized, HISCO-coded and classified occupational titles, release 2018.01 (IISG Amsterdam 2018). And off course further citations are needed to HISCO: Marco H. D. van Leeuwen, Ineke Maas and Andrew Miles, HISCO. Historical International Standard Classification of Occupations (Leuven University Press 2002) and to the subsequent sources of used classifications.
DOI: 10.5683/sp3/ceq4kf
title: Total mercury, methylmercury, nitrogen, carbon, and sulfur concentrations in thermokarst lake banks in Old Crow Flats, Yukon, Canada

description: This dataset provides biogeochemical information on soil samples collected on nine thermokarst lake banks in Old Crow Flats, Yukon, Canada. The banks of the lakes were characterized based on erosion level (Agressive erosion: pieces of ground are falling off and being eroded away by the waves; Subsidence: ground subsidence due to permafrost thaw) and vegetation (graminoids, small shrubs &lt;1m, tall shrubs ≥1m). For each bank, samples were collected from three locations: the top of the bank (BT), the emerged foot of the bank (BF_EM), and the submerged foot of the bank (BF_SUB). Analysis included soil total mercury (sTHg), methylmercury (MeHg), nitrogen (N), carbon (C), and sulfur (S) concentrations, as well as loss-on-ignition (LOI).
DOI: 10.34894/wmv2lb
title: Standardized, HISCO-coded and classified occupational titles, release 2020.02
title: Standardized, HISCO-coded and classified occupational titles, release 2020.02
title: HSN HISCO Release 2020.02

description: This release consists of a standardization of 281,355 different occupational titles found in the sources used by the HSN till the 1st of January 2020 and the coding of the standardized occupations in the HISCO, HISCLASS, OCC1950, HISCAM and SOCPO classifications. HSN stands for the Historical Sample of the Netherlands which forms a database consisting of over 85,000 life courses and which organizes adjacent projects with more life course data like the LINKS project (see www.iisg.nl\hsn). 

The coding process of the occupational titles followed three stages:
1) All titles were standardized into a uniform spelling,
2) The resulting standards were provided with a HISCO code,
3) On the basis of the HISCO code other classifications such as HISCLASS and HISCAM were added to the standards.

To be cited as: Kees Mandemakers, Jan Hornix, Rick J. Mourits, Sanne Muurling, Corinne Boter, Ingrid K. van Dijk, Ineke Maas, Bart Van de Putte, Richard L. Zijdeman, Paul Lambert, Marco H.D. van Leeuwen, Frans van Poppel and Andrew Miles, HSN standardized, HISCO-coded and classified occupational titles, HSN release 2020.02 (IISG Amsterdam 2020).
DOI: 10.5281/zenodo.17792612
title: Supporting Data for article "Towards a Strong Future for Marine Social Science for Sustainability"

description: Supporting data from systematic scoping review and working group discussion results in support of manuscript entitled "Towards a Strong Future for Marine Social Science for Sustainability."
DOI: 10.5281/zenodo.14932532
title: Supporting Data for article "Towards a Strong Future for Marine Social Science for Sustainability"

description: Supporting data from systematic scoping review and working group discussion results in support of manuscript entitled "Towards a Strong Future for Marine Social Science for Sustainability."
DOI: 10.34894/o7ey8b
title: HSNDB index for exposure to infectious disease

description: The HSNDB index for occupational exposure to infectious disease shows for all HISCO unit groups, also known as three-digit HISCO codes, occupational exposure to infectious disease on two dimensions:

1. Working in an indoor environment (no/yes)
2. Regular social interaction (no/yes)
DOI: 10.17026/ar/vvnpw4
title: PAN-00066898 - Early Medieval triangular mount (belt)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00066898&lt;/p&gt;
description: Modified: 2025-08-05T14:13:03+02:00
DOI: 10.15468/dl.j6ugrd
title: Occurrence Download

description: A dataset containing 8000 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((151.588406 -32.767296, 151.547605 -32.728785, 151.498088 -32.728785, 151.538876 -32.767296, 151.588406 -32.767296))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 8000 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022632-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.