cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15300502
title: Drug trade messages on Tor in Finnish, English and Polish

description: Within the Tor network, we compared the Finnish, English and Polish drug trade. To accomplish this, we selected three active onion websites. We compile and publish these three datasets, freely available to the academic community.

1. A Finnish website within the Tor network offers an anonymous chat messenger and chat rooms for the biggest towns in Finland, where most of the messages are associated with the illicit drug trade. Finnish sample / Finnish.zip: 1,500 messages from the Finnish onion website “Tsatti”. We collected these in November and December of 2022. Anonymous chat Tsatti—Finnish discussions on Tor: http://tsattickdplsh2i2xqzlybvreiuppgoqsicmzkrotuudnk7h665ukgid.onion/

2. The Polish forum website “Cebulka” operates anonymously within Tor and facilitates drug trafficking. Polish sample / Polish.zip: 965 advertisement pages from the “Cebulka” onion website in Polish. We collected these pages in January 2023. Anonymous Forum Cebulka: http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/

3. Established in 2021, “Nemesis” is a combination of a darknet market and a forum; in addition, the products are displayed publicly, and registration is not necessary. English sample / English.zip: “Nemesis” Market offers illegal products. Data consists of 1,334 pages. We collected these pages in January 2023. Anonymous Marketplace Nemesis Market: http://nemesis555nchzn2dogee6mlc7xxgeeshqirmh3yzn4lo5cnd4s5a4yd.onion/




Language and sellers
Unique sellers in the sample
Selling Advertisements


English
223
1334


Polish
>200
965


Finnish
>200
>500







Language and contact methods
Email
Wickr
Session
Telegram


English
20
2
0
13


Polish
1864
698
15
91


Finnish
0
588
35
9




The datasets are under CC BY 4.0 license: You are free to copy, share, redistribute, remix, transform, and build upon the material for any purpose, even commercially. You must give attribution and appropriate credit. Uncompressed data contains a “README.md” file, all of the web pages in a JSON file in a machine-readable format, screenshots from the websites and a “textpages” folder contains readable text versions of the web pages.

Data collector and curation: Juha Nurmi

Funding: This work was supported by the European Commission under the Horizon Europe funding programme, as part of the project SafeHorizon (Grant Agreement 101168562). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European. Neither the European Union nor the granting authority can be held responsible for them. The authors declare no competing interests.
DOI: 10.5281/zenodo.15300503
title: Drug trade messages on Tor in Finnish, English and Polish

description: Within the Tor network, we compared the Finnish, English and Polish drug trade. To accomplish this, we selected three active onion websites. We compile and publish these three datasets, freely available to the academic community.

1. A Finnish website within the Tor network offers an anonymous chat messenger and chat rooms for the biggest towns in Finland, where most of the messages are associated with the illicit drug trade. Finnish sample / Finnish.zip: 1,500 messages from the Finnish onion website “Tsatti”. We collected these in November and December of 2022. Anonymous chat Tsatti—Finnish discussions on Tor: http://tsattickdplsh2i2xqzlybvreiuppgoqsicmzkrotuudnk7h665ukgid.onion/

2. The Polish forum website “Cebulka” operates anonymously within Tor and facilitates drug trafficking. Polish sample / Polish.zip: 965 advertisement pages from the “Cebulka” onion website in Polish. We collected these pages in January 2023. Anonymous Forum Cebulka: http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/

3. Established in 2021, “Nemesis” is a combination of a darknet market and a forum; in addition, the products are displayed publicly, and registration is not necessary. English sample / English.zip: “Nemesis” Market offers illegal products. Data consists of 1,334 pages. We collected these pages in January 2023. Anonymous Marketplace Nemesis Market: http://nemesis555nchzn2dogee6mlc7xxgeeshqirmh3yzn4lo5cnd4s5a4yd.onion/




Language and sellers
Unique sellers in the sample
Selling Advertisements


English
223
1334


Polish
>200
965


Finnish
>200
>500







Language and contact methods
Email
Wickr
Session
Telegram


English
20
2
0
13


Polish
1864
698
15
91


Finnish
0
588
35
9




The datasets are under CC BY 4.0 license: You are free to copy, share, redistribute, remix, transform, and build upon the material for any purpose, even commercially. You must give attribution and appropriate credit. Uncompressed data contains a “README.md” file, all of the web pages in a JSON file in a machine-readable format, screenshots from the websites and a “textpages” folder contains readable text versions of the web pages.

Data collector and curation: Juha Nurmi

Funding: This work was supported by the European Commission under the Horizon Europe funding programme, as part of the project SafeHorizon (Grant Agreement 101168562). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European. Neither the European Union nor the granting authority can be held responsible for them. The authors declare no competing interests.
DOI: 10.17026/ar/hhcaqs
title: PAN-00104153 - coin/coin-related, Trajanus (98-117), denarius

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00104153&lt;/p&gt;
description: Modified: 2025-04-28T11:04:45+02:00
DOI: 10.17632/c8xyt5sdtt
title: Supplemental Materials

description: Supplementary materials for Chida et al., 2025.
DOI: 10.17632/c8xyt5sdtt.3
title: Supplemental Materials

description: Supplementary materials for Chida et al., 2025.
DOI: 10.15468/dl.kecawd
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.14632056396786 -1.5885054009233386,-74.14632056396786 -1.5885054009233386,-74.14632056396786 2.4114945990766614,-78.14632056396786 2.4114945990766614,-78.14632056396786 -1.5885054009233386))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Atelopus spumarius"
 ]
}

 The dataset's 2 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016173-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/xaw9ae
title: PAN-00085082 - Roman rotary or lift-slide key

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00085082&lt;/p&gt;
description: Modified: 2025-07-18T15:18:35+02:00
DOI: 10.17632/xzgy3dtt5v.1
title: A Deep Learning-Based Interactive Decision-making Framework for Low-Carbon Vertical Green Production Renewal in Residential Areas
DOI: 10.17632/v79dvnb34z
title: Building the Climate-Responsive Environmental Theory of Nursing (CRETN): A Q Methodology Study of Nurses’ Perspectives on Climate-Driven Care Challenges

description: This study, conducted by Dr. Fernan N. Torreno and Frincess Flores, examined the impact of climate change on nursing practice and introduced the Climate-Responsive Environmental Theory of Nursing (CRETN) to guide the profession in an era of environmental instability. Nurses, who represent the majority of the global health workforce, are at the frontline of climate-related crises such as heatwaves, floods, hurricanes, and wildfire smoke, yet traditional frameworks like Florence Nightingale’s Environmental Theory no longer fully capture these challenges. To address this gap, the research used a mixed-methods design integrating Q methodology, qualitative interviews, and systematic review with meta-analysis. In the Q study, 100 nurses sorted 45 climate-related statements across a forced distribution grid, producing four distinct perspectives: burnout and resilience, absenteeism and safety, missed care and system fragility, and medication errors under heat stress. These factors together explained 84% of variance, reflecting robust thematic patterns. Follow-up interviews with 18 nurses enriched the interpretation with personal accounts of stress, coping, and patient care under climate pressures. A systematic review of 38 studies published between 2000 and 2025 confirmed these findings, with pooled meta-analysis results showing significant associations between heatwaves and burnout (OR 1.45), wildfire smoke and absenteeism (OR 1.30), floods and hurricanes and missed care (OR 1.55), and extreme heat and medication errors (OR 1.25). From these insights, Dr. Torreno and Flores extended Nightingale’s model into CRETN, which integrates environmental stressors, nurse outcomes, patient outcomes, and system-level moderators such as staffing and infrastructure. The study concludes that nursing education must incorporate climate literacy, practice should prioritize occupational safety, and policies must strengthen climate-resilient health systems. CRETN thus provides a contemporary framework for advancing nursing knowledge, practice, and advocacy in the face of climate change.
DOI: 10.17632/v79dvnb34z.1
title: Building the Climate-Responsive Environmental Theory of Nursing (CRETN): A Q Methodology Study of Nurses’ Perspectives on Climate-Driven Care Challenges

description: This study, conducted by Dr. Fernan N. Torreno and Frincess Flores, examined the impact of climate change on nursing practice and introduced the Climate-Responsive Environmental Theory of Nursing (CRETN) to guide the profession in an era of environmental instability. Nurses, who represent the majority of the global health workforce, are at the frontline of climate-related crises such as heatwaves, floods, hurricanes, and wildfire smoke, yet traditional frameworks like Florence Nightingale’s Environmental Theory no longer fully capture these challenges. To address this gap, the research used a mixed-methods design integrating Q methodology, qualitative interviews, and systematic review with meta-analysis. In the Q study, 100 nurses sorted 45 climate-related statements across a forced distribution grid, producing four distinct perspectives: burnout and resilience, absenteeism and safety, missed care and system fragility, and medication errors under heat stress. These factors together explained 84% of variance, reflecting robust thematic patterns. Follow-up interviews with 18 nurses enriched the interpretation with personal accounts of stress, coping, and patient care under climate pressures. A systematic review of 38 studies published between 2000 and 2025 confirmed these findings, with pooled meta-analysis results showing significant associations between heatwaves and burnout (OR 1.45), wildfire smoke and absenteeism (OR 1.30), floods and hurricanes and missed care (OR 1.55), and extreme heat and medication errors (OR 1.25). From these insights, Dr. Torreno and Flores extended Nightingale’s model into CRETN, which integrates environmental stressors, nurse outcomes, patient outcomes, and system-level moderators such as staffing and infrastructure. The study concludes that nursing education must incorporate climate literacy, practice should prioritize occupational safety, and policies must strengthen climate-resilient health systems. CRETN thus provides a contemporary framework for advancing nursing knowledge, practice, and advocacy in the face of climate change.
DOI: 10.17026/ar/wlynup
title: PAN-00104155 - coin/coin-related, dupondius/as

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00104155&lt;/p&gt;
description: Modified: 2025-04-28T11:37:06+02:00
DOI: 10.57931/2479357
title: County Level Annual Population Projections for SSP3 and SSP5

description: This dataset consist of annual (2020-2100) county-level population projections for the United States (U.S.) for Shared Socioeconomic Pathways (SSPs) 3 and 5. The original decadal state-level data is used as an input to the gridded population data model to downscale the state-level projections for each SSP to a 1 km grid. The 1 km data is then aggregated to the county-level using the 2020 TIGER/Line county shapefiles from the U.S. Census Bureau.
The two data files are shared as .csv files with the following structure:

Rows = Data for individual counties which are identified using their Federal Information Processing Standard (FIPS) code. The FIPS code is stored in the first column. 
Columns = Each year from 2020-2100 is an individual column. For easier reference, the state name associated with each row is stored in the last column.
Values in each cell are the downscaled estimated population for that county-year combination. Values are fractional due to the weighting scheme used in the downscaling.
The paper and model source code cited in the "Related Works" section describes the initial source of the population projections.
DOI: 10.34847/nkl.e1115bkl
title: FRA2019_0052

description: Fichier source (TGD).
description: Fichier source (WAV).
description: Fichier dérivé (TEI).
DOI: 10.17026/ar/vcwhuk
title: PAN-00127230 - ingot

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00127230&lt;/p&gt;
description: Modified: 2025-07-10T11:36:08+02:00
DOI: 10.17632/bv6bgvcdgs
title: Friend or Foe: A Q Methodological Study of Skill-Based Mathematics Questions

description: The data were collected through one-on-one sessions with the participating teachers and students as a part of Q-methodological study. Prior to data collection, each Q statement developed by the researcher was printed on separate sheets of paper and duplicated according to the number of participants. These were then distributed to the teachers and students. Participants were first asked to sort the statements into three groups: agree, disagree, and neutral. Then, they were instructed to read each Q statement and place it under the number that best represented their level of agreement or disagreement, using the grid ranging from -3 to +3. This dataset includes distribution of 20 mathematics teachers' and 20 high school students' responses on Q-grid to 18 Q-statements.
DOI: 10.17632/bv6bgvcdgs.1
title: Friend or Foe: A Q Methodological Study of Skill-Based Mathematics Questions

description: The data were collected through one-on-one sessions with the participating teachers and students as a part of Q-methodological study. Prior to data collection, each Q statement developed by the researcher was printed on separate sheets of paper and duplicated according to the number of participants. These were then distributed to the teachers and students. Participants were first asked to sort the statements into three groups: agree, disagree, and neutral. Then, they were instructed to read each Q statement and place it under the number that best represented their level of agreement or disagreement, using the grid ranging from -3 to +3. This dataset includes distribution of 20 mathematics teachers' and 20 high school students' responses on Q-grid to 18 Q-statements.
DOI: 10.17632/h7gp828zjx
title: Arbitration-Delhi HC-Snapshot

description: This is a snapshot of results obtained through processing of a larger dataset, which itself is available at https://figshare.com/articles/dataset/Arbitration_-_Delhi_HC_-_Dataset/30168439
DOI: 10.17632/h7gp828zjx.1
title: Arbitration-Delhi HC-Snapshot

description: This is a snapshot of results obtained through processing of a larger dataset, which itself is available at https://figshare.com/articles/dataset/Arbitration_-_Delhi_HC_-_Dataset/30168439
DOI: 10.5281/zenodo.17202684
title: Table 1 in Ray spider rush: Fast-tracking integrative taxonomy in Panama's cloud forests

description: Table 1. Codes used for specimens, indicating the sampling protocols for the semiquatitative analyses, locality, method, sample number, time, day, collector and specimen number. Every specimen received a unique identifier; for example, SFU1NAA029 indicates: NP Fortuna, looking up, first sample at night, 10 June 2007, collected by M. Arnedo, specimen 029 in the sample.
ProjectLocality (125 samples each)MethodSample number (one hour each)TimeDayCollectorSpecimen numberPancoding Spiders SFortuna FBeating Bfirst 1Day D7 Jun. 2007 7Miquel Arnedo A3 digits ###Campana CCryptic Csecond 2Night N8 Jun. 2007 8Dimitar Dimitrov DOmar Torrijos Herrera TLooking down D9 Jun. 2007 9Gustavo Hormiga HLa Amistad ALooking up U10 Jun. 2007 AFacundo Labarque L11 Jun. 2007 BMartín Ramírez R12 Jun. 2007 CLigia Benavides B14 Jun. 2007 DLuis Piacentini P15 Jun. 2007 E16 Jun. 2007 F17 Jun. 2007 G18 Jun. 2007 H19 Jun. 2007 I4 Jun. 2008 45 Jun. 2008 56 Jun. 2008 67 Jun. 2008 78 Jun. 2008 89 Jun. 2008 912 Jun. 2008 C13 Jun. 2008 D14 Jun. 2008 E15 Jun. 2008 F16 Jun. 2008 G17 Jun. 2008 H
description: Published as part of Labarque, Facundo M., Piacentini, Luis N., Pons, Joan, Hormiga, Gustavo, Arnedo, Miquel A. & Ramírez, Martín J., 2025, Ray spider rush: Fast-tracking integrative taxonomy in Panama's cloud forests, pp. 5 in European Journal of Taxonomy 1010 on page 5, DOI: 10.5852/ejt.2025.1010.3021, http://zenodo.org/record/17202080
DOI: 10.5281/zenodo.17202685
title: Table 1 in Ray spider rush: Fast-tracking integrative taxonomy in Panama's cloud forests

description: Table 1. Codes used for specimens, indicating the sampling protocols for the semiquatitative analyses, locality, method, sample number, time, day, collector and specimen number. Every specimen received a unique identifier; for example, SFU1NAA029 indicates: NP Fortuna, looking up, first sample at night, 10 June 2007, collected by M. Arnedo, specimen 029 in the sample.
ProjectLocality (125 samples each)MethodSample number (one hour each)TimeDayCollectorSpecimen numberPancoding Spiders SFortuna FBeating Bfirst 1Day D7 Jun. 2007 7Miquel Arnedo A3 digits ###Campana CCryptic Csecond 2Night N8 Jun. 2007 8Dimitar Dimitrov DOmar Torrijos Herrera TLooking down D9 Jun. 2007 9Gustavo Hormiga HLa Amistad ALooking up U10 Jun. 2007 AFacundo Labarque L11 Jun. 2007 BMartín Ramírez R12 Jun. 2007 CLigia Benavides B14 Jun. 2007 DLuis Piacentini P15 Jun. 2007 E16 Jun. 2007 F17 Jun. 2007 G18 Jun. 2007 H19 Jun. 2007 I4 Jun. 2008 45 Jun. 2008 56 Jun. 2008 67 Jun. 2008 78 Jun. 2008 89 Jun. 2008 912 Jun. 2008 C13 Jun. 2008 D14 Jun. 2008 E15 Jun. 2008 F16 Jun. 2008 G17 Jun. 2008 H
description: Published as part of Labarque, Facundo M., Piacentini, Luis N., Pons, Joan, Hormiga, Gustavo, Arnedo, Miquel A. & Ramírez, Martín J., 2025, Ray spider rush: Fast-tracking integrative taxonomy in Panama's cloud forests, pp. 5 in European Journal of Taxonomy 1010 on page 5, DOI: 10.5852/ejt.2025.1010.3021, http://zenodo.org/record/17202080
DOI: 10.15468/dl.pyhx2x
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.46158 -33.999683, 148.46158 -34.00642, 148.472809 -34.00642, 148.472809 -33.999683, 148.46158 -33.999683)), ((148.472809 -34.00642, 148.472809 -34.008666, 148.475054 -34.008666, 148.475054 -34.00642, 148.472809 -34.00642)), ((148.475054 -34.008666, 148.475054 -34.010912, 148.484038 -34.010912, 148.484038 -34.008666, 148.475054 -34.008666)))"
 ]
}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059717-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4em9b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.573869 -33.999683, 148.573869 -34.001929, 148.576115 -34.001929, 148.576115 -33.999683, 148.573869 -33.999683))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059718-240626123714530/datasets/export for details.
DOI: 10.15468/dl.x5us9x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.612048 -33.999683, 148.614294 -33.999683, 148.614294 -33.995191, 148.618785 -33.995191, 148.618785 -33.988454, 148.61654 -33.988454, 148.61654 -33.986208, 148.614294 -33.986208, 148.614294 -33.992946, 148.612048 -33.992946, 148.612048 -33.999683)), ((148.612048 -33.999683, 148.607556 -33.999683, 148.607556 -34.001929, 148.612048 -34.001929, 148.612048 -33.999683)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059720-240626123714530/datasets/export for details.
DOI: 10.15468/dl.mf4brm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.020783 -33.999683, 149.020783 -34.001929, 149.025275 -34.001929, 149.025275 -33.999683, 149.020783 -33.999683))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059721-240626123714530/datasets/export for details.
DOI: 10.15468/dl.j2tec8
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eugenia azurensis O.Berg"
 ]
}

 The dataset includes 43 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059722-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2bj56j
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.476681 -33.999683, 149.481172 -33.999683, 149.481172 -33.992946, 149.485664 -33.992946, 149.485664 -33.9907, 149.48791 -33.9907, 149.48791 -33.986208, 149.476681 -33.986208, 149.476681 -33.999683)), ((149.476681 -33.999683, 149.472189 -33.999683, 149.472189 -34.001929, 149.476681 -34.001929, 149.476681 -33.999683)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059723-240626123714530/datasets/export for details.
DOI: 10.15468/dl.xa9s7y
title: Occurrence Download

description: A dataset containing 117090 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alces Gray, 1821"
 ]
}

 The dataset includes 117090 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059719-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3phea
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.904002 -34.001929, 148.904002 -34.008666, 148.906248 -34.008666, 148.906248 -34.001929, 148.904002 -34.001929))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059725-240626123714530/datasets/export for details.
DOI: 10.15468/dl.qqqvm3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.061208 -34.001929, 149.061208 -34.00642, 149.063454 -34.00642, 149.063454 -34.001929, 149.061208 -34.001929))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059726-240626123714530/datasets/export for details.