cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14012168
title: Russian state institutions full-text datasets – A collection of corpora based on contents extracted from the websites of Russian state institutions

description: This is a collection of full-text datasets based on contents extracted from the websites of Russian state institutions.

All datasets do not include items published after 31 December 2023.

These datasets have been introduced in the following book chapter, which offers additional context:

> Comai, Giorgio (2025, forthcoming), "Text-mining on-line sources from Russia openly", in *Autocracy, Influence, War: Russian Propaganda Today*, edited by Paul Goode

The name of each corpus is composed of the bare domain name, a two letter code of the main language of the contents, and the year of release of the dataset, separated by an underscore, e.g. `kremlin.ru_ru_2024` for the Russian-language version of Kremlin.ru.

This release includes the following websites:
- Russia's president, kremlin.ru, in English, filename: kremlin.ru_en_2024, from 1999-12-31 to 2023-12-31. Items included: 33 165
- Russia's president, kremlin.ru, in Russian, filename: kremlin.ru_ru_2024, from 1999-12-31 to 2023-12-31. Items included: 45 538
- Russia's MFA, mid.ru, in English, filename: mid.ru_en_2024, from 2003-01-04 to 2023-12-31. Items included: 25 943
- Russia's MFA, mid.ru, in Russian, filename: mid.ru_ru_2024, from 2003-01-02 to 2023-12-31. Items included: 56 203
- Russia's government, government.ru, in Russian, filename: government.ru_ru_2024, from 2006-06-22 to 2023-12-30. Items included: 17 135
- Russia's government (archived version), archive.government.ru, in Russian, filename: archive.government.ru_ru_2024, from 2008-05-07 to 2013-05-21. Items included: 7 103
- Russia's prime minister (archived version), archive.premier.gov.ru, in Russian, filename: archive.premier.gov.ru_ru_2024, from 2008-05-07 to 2012-05-07. Items included: 3 323
- Russia's Duma, duma.gov.ru, in Russian, filename: duma.gov.ru_ru_2024, from 2006-04-05 to 2023-12-30. Items included: 29 094
- Russia's Duma (transcripts), transcript.duma.gov.ru, in Russian, filename: transcript.duma.gov.ru_ru_2024, from 1994-01-11 to 2023-12-15. Items included: 6 032

File formats: compressed csv files (.csv.gz); Open Document Spreadsheets (.ods)

A web version of the documentation accompanying this release is available online:
https://tadadit.xyz/datasets/2024/russian_institutions_2024/

Explore through a basic web interface:
https://explore.tadadit.xyz/2024/ru_institutions_2024/
description: Published on Discuss Data, https://discuss-data.net/dataset/0578d7fe-35f7-4e9e-a29d-926618a5c6bd/
description: Method(s) of data collection: Text MiningMethod(s) of data analysis: Word Frequency
DOI: 10.5281/zenodo.14012169
title: Russian state institutions full-text datasets – A collection of corpora based on contents extracted from the websites of Russian state institutions

description: This is a collection of full-text datasets based on contents extracted from the websites of Russian state institutions.

All datasets do not include items published after 31 December 2023.

These datasets have been introduced in the following book chapter, which offers additional context:

> Comai, Giorgio (2025, forthcoming), "Text-mining on-line sources from Russia openly", in *Autocracy, Influence, War: Russian Propaganda Today*, edited by Paul Goode

The name of each corpus is composed of the bare domain name, a two letter code of the main language of the contents, and the year of release of the dataset, separated by an underscore, e.g. `kremlin.ru_ru_2024` for the Russian-language version of Kremlin.ru.

This release includes the following websites:
- Russia's president, kremlin.ru, in English, filename: kremlin.ru_en_2024, from 1999-12-31 to 2023-12-31. Items included: 33 165
- Russia's president, kremlin.ru, in Russian, filename: kremlin.ru_ru_2024, from 1999-12-31 to 2023-12-31. Items included: 45 538
- Russia's MFA, mid.ru, in English, filename: mid.ru_en_2024, from 2003-01-04 to 2023-12-31. Items included: 25 943
- Russia's MFA, mid.ru, in Russian, filename: mid.ru_ru_2024, from 2003-01-02 to 2023-12-31. Items included: 56 203
- Russia's government, government.ru, in Russian, filename: government.ru_ru_2024, from 2006-06-22 to 2023-12-30. Items included: 17 135
- Russia's government (archived version), archive.government.ru, in Russian, filename: archive.government.ru_ru_2024, from 2008-05-07 to 2013-05-21. Items included: 7 103
- Russia's prime minister (archived version), archive.premier.gov.ru, in Russian, filename: archive.premier.gov.ru_ru_2024, from 2008-05-07 to 2012-05-07. Items included: 3 323
- Russia's Duma, duma.gov.ru, in Russian, filename: duma.gov.ru_ru_2024, from 2006-04-05 to 2023-12-30. Items included: 29 094
- Russia's Duma (transcripts), transcript.duma.gov.ru, in Russian, filename: transcript.duma.gov.ru_ru_2024, from 1994-01-11 to 2023-12-15. Items included: 6 032

File formats: compressed csv files (.csv.gz); Open Document Spreadsheets (.ods)

A web version of the documentation accompanying this release is available online:
https://tadadit.xyz/datasets/2024/russian_institutions_2024/

Explore through a basic web interface:
https://explore.tadadit.xyz/2024/ru_institutions_2024/
description: Published on Discuss Data, https://discuss-data.net/dataset/0578d7fe-35f7-4e9e-a29d-926618a5c6bd/
description: Method(s) of data collection: Text MiningMethod(s) of data analysis: Word Frequency
DOI: 10.15468/dl.aa7ydr
title: Occurrence Download

description: A dataset listing the 20 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is one of (Norway, Sweden)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Margaritifera margaritifera (Linnaeus, 1758), Phoxinus phoxinus (Linnaeus, 1758), Salvelinus namaycush (Walbaum, 1792), Triturus cristatus (Laurenti, 1768), Esox lucius Linnaeus, 1758, Perca fluviatilis Linnaeus, 1758, Rutilus rutilus (Linnaeus, 1758), Cottus gobio Linnaeus, 1758, Salvelinus fontinalis (Mitchill, 1814), Coregonus lavaretus (Linnaeus, 1758), Elodea canadensis Michx.)"
 ]
}

 The dataset's 20 records were derived from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011971-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/c3a4912b-b248-4ad2-8c73-15b505f4287f
title: Supporting code and data for: Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data

description: This repository contains some supporting data and code for the 2024 SESAR Innovation Days Conference paper "Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data."<br>Note that this is a continuation of the work published here:<br><strong>Morfin Veytia, Andres; Ellerbroek, Joost; Hoekstra, Jacco (2024): Supporting data and code for Decentralised Traffic Management for Constrained Urban Airspace: Dynamically Generating and Acting Upon Aggregate Flow Data. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/54825f14-8743-447d-8346-3afa46d319d6.v2</strong><br>Therefore, much of the data and code is similar. However, this work provides some additional code and scenarios.<br>The main components need to reproduce the results are:<br>BlueSky simulator code to reproduce the simulations.Post processing code for the scenarios to generate the plots in the paper and logs.Voronoi creation codePython environment description.<br><strong>1. BlueSky Simulator code</strong>This includes the BlueSky code for simulating the scenarios. This is the bluesky.zip folder. Note that the code provided is a condensed version of the one in https://github.com/amorfinv/bluesky/tree/rotterdam. The plugins and scenarios are also provided in the simulator code. The plugins are based on those based in the following repository, https://github.com/amorfinv/bluesky_plugins.<br>Refer to the README.md file provided to learn how to run the scenarios. Also, make sure to install a compatible python environment.<br><strong>2. Post-processing code, plots, and logs</strong>This includes the code to generate the plots seen in the paper and the logs of the simulations. It also includes some additional plots not shown in the paper. Read the README.md file for recreating the plots. This information can be found in main_experiment_results.zip. Some of the logs come from the previous work. The previous logs are labelled as real-time data labelling in this paper.<br><strong>3. Voronoi creation code</strong>The file called generate_voronois.zip includes the code to generate the voronoi code used for the historical data concept in the paper. Note that to generate the voronoi a more recent version of geopandas is necessary, so a different python environment is required. All you need is python=3.12, geopandas=1.0.1 and scikit-learn=1.5.1.<br><strong>6. Python environment description</strong>This includes the python environment used to simulate, post-process, and generate the plots. This work used conda environments. The main packages used are those required by BlueSky in addition to geopandas, osmnx, and seaborn. Note that the voronoi creation requires a different python environment.
DOI: 10.4121/c3a4912b-b248-4ad2-8c73-15b505f4287f.v1
title: Supporting code and data for: Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data

description: This repository contains some supporting data and code for the 2024 SESAR Innovation Days Conference paper "Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data."<br>Note that this is a continuation of the work published here:<br><strong>Morfin Veytia, Andres; Ellerbroek, Joost; Hoekstra, Jacco (2024): Supporting data and code for Decentralised Traffic Management for Constrained Urban Airspace: Dynamically Generating and Acting Upon Aggregate Flow Data. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/54825f14-8743-447d-8346-3afa46d319d6.v2</strong><br>Therefore, much of the data and code is similar. However, this work provides some additional code and scenarios.<br>The main components need to reproduce the results are:<br>BlueSky simulator code to reproduce the simulations.Post processing code for the scenarios to generate the plots in the paper and logs.Voronoi creation codePython environment description.<br><strong>1. BlueSky Simulator code</strong>This includes the BlueSky code for simulating the scenarios. This is the bluesky.zip folder. Note that the code provided is a condensed version of the one in https://github.com/amorfinv/bluesky/tree/rotterdam. The plugins and scenarios are also provided in the simulator code. The plugins are based on those based in the following repository, https://github.com/amorfinv/bluesky_plugins.<br>Refer to the README.md file provided to learn how to run the scenarios. Also, make sure to install a compatible python environment.<br><strong>2. Post-processing code, plots, and logs</strong>This includes the code to generate the plots seen in the paper and the logs of the simulations. It also includes some additional plots not shown in the paper. Read the README.md file for recreating the plots. This information can be found in main_experiment_results.zip. Some of the logs come from the previous work. The previous logs are labelled as real-time data labelling in this paper.<br><strong>3. Voronoi creation code</strong>The file called generate_voronois.zip includes the code to generate the voronoi code used for the historical data concept in the paper. Note that to generate the voronoi a more recent version of geopandas is necessary, so a different python environment is required. All you need is python=3.12, geopandas=1.0.1 and scikit-learn=1.5.1.<br><strong>6. Python environment description</strong>This includes the python environment used to simulate, post-process, and generate the plots. This work used conda environments. The main packages used are those required by BlueSky in addition to geopandas, osmnx, and seaborn. Note that the voronoi creation requires a different python environment.
DOI: 10.25413/sun.27331170
title: Cancer diagnostic service use in people living with HIV in South Africa: A cross-sectional study

description: The objective of this study was to map place of cancer diagnosis in relation to Human Immunodeficiency Virus (HIV) care centre among people living with HIV (PLHIV) within South Africa (SA) using national laboratory database.We linked HIV and cancer laboratory data from 2004–2014 using supervised machine-learning algorithms. We performed a cross-sectional analysis comparing province where individuals accessed their HIV care versus where they had their cancer diagnosis.We used laboratory test records related to HIV diagnostics and care, such as CD4 cell counts and percentages, rapid tests, qualitative Polymerase Chain Reaction (PCR), antibody and antigen tests for HIV data that was documented as HIV positive and laboratory diagnosed cancer records from SA.Our study population consisted of HIV records from the National Health Laboratory Service (NHLS) that linked to cancer record at the National Cancer Registry (NCR) between 2004–2014.We linked HIV records from NHLS to cancer records at NCR in order to study the inherent characteristics of the population with both HIV and cancer.The study population was 68,284 individuals with cancer and documented HIV related laboratory test. The median age at cancer diagnosis was 40 [IQR, 33–48] years for the study population with most cancers in PLHIV diagnosed in females 70.9% [n = 46,313]. Of all the PLHIV and cancer, 25% (n = 16,364 p &lt; 0.001) sought treatment outside their province of residence with 60.7% (n = 10,235) travelling to Gauteng. KZN had 46.6% (n = 4,107) of its PLHIV getting cancer diagnosis in Gauteng. Western Cape had 95% (n = 6,200) of PLHIV getting cancer diagnosis within the province.Our results showed health systems inequalities across provinces in SA with respect to cancer diagnosis. KZN for example had nearly half of the PLHIV getting cancer diagnosis outside the province while Western Cape is able to offer cancer diagnostic services to most of the PLHIV in the province. Gauteng is getting over burdened with referral for cancer diagnosis from other provinces. More effort is required to ensure equitable access to cancer diagnostic services within the country.
DOI: 10.25413/sun.27331170.v1
title: Cancer diagnostic service use in people living with HIV in South Africa: A cross-sectional study

description: The objective of this study was to map place of cancer diagnosis in relation to Human Immunodeficiency Virus (HIV) care centre among people living with HIV (PLHIV) within South Africa (SA) using national laboratory database.We linked HIV and cancer laboratory data from 2004–2014 using supervised machine-learning algorithms. We performed a cross-sectional analysis comparing province where individuals accessed their HIV care versus where they had their cancer diagnosis.We used laboratory test records related to HIV diagnostics and care, such as CD4 cell counts and percentages, rapid tests, qualitative Polymerase Chain Reaction (PCR), antibody and antigen tests for HIV data that was documented as HIV positive and laboratory diagnosed cancer records from SA.Our study population consisted of HIV records from the National Health Laboratory Service (NHLS) that linked to cancer record at the National Cancer Registry (NCR) between 2004–2014.We linked HIV records from NHLS to cancer records at NCR in order to study the inherent characteristics of the population with both HIV and cancer.The study population was 68,284 individuals with cancer and documented HIV related laboratory test. The median age at cancer diagnosis was 40 [IQR, 33–48] years for the study population with most cancers in PLHIV diagnosed in females 70.9% [n = 46,313]. Of all the PLHIV and cancer, 25% (n = 16,364 p &lt; 0.001) sought treatment outside their province of residence with 60.7% (n = 10,235) travelling to Gauteng. KZN had 46.6% (n = 4,107) of its PLHIV getting cancer diagnosis in Gauteng. Western Cape had 95% (n = 6,200) of PLHIV getting cancer diagnosis within the province.Our results showed health systems inequalities across provinces in SA with respect to cancer diagnosis. KZN for example had nearly half of the PLHIV getting cancer diagnosis outside the province while Western Cape is able to offer cancer diagnostic services to most of the PLHIV in the province. Gauteng is getting over burdened with referral for cancer diagnosis from other provinces. More effort is required to ensure equitable access to cancer diagnostic services within the country.
DOI: 10.5284/1116860
title: Digital Archive for Hornsea Offshore Wind Farm Project One (HOW01) 2015-2019

description: This collection comprises fieldwork and post-excavation data associated with Hornsea Offshore Wind Farm Project One. Archaeological work was undertaken by Wessex Archaeology between September 2015 and July 2019 and comprised seven set piece excavation areas, seven strip, map and record areas, twenty targeted watching brief areas and a general watching brief along the route of cable through North Lincolnshire, Northeast Lincolnshire, and Lincolnshire. A historic building recording of a WWII dispersed airfield site was also undertaken.
DOI: 10.34740/kaggle/dsv/9762617
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.6084/m9.figshare.27331246
title: Coping strategies used by migrant healthcare workers to support their mental health during COVID-19 in the United Kingdom: a qualitative analysis

description: <b>Background:</b> The incidence of mental illness has risen since the coronavirus disease 2019 (COVID-19) pandemic. The number of healthcare workers (HCWs) needing mental health support has increased significantly. <b>Objective:</b> This secondary analysis of qualitative data explored the coping strategies of migrant HCWs living in the UK during the COVID-19 pandemic. Our aim was to identify the coping strategies used by migrant HCWs, and how they could be explored post-pandemic as support mechanisms of an increasingly diverse workforce. <b>Method:</b> As part of the United Kingdom Research study into Ethnicity And COVID-19 outcomes among Healthcare workers (UK-REACH), we conducted in-depth semi-structured interviews and focus groups with clinical and non-clinical HCWs across the UK, on Microsoft Teams, from December 2020 to July 2021. We conducted a thematic analysis using Braun and Clarke’s framework to explore the lived experiences of HCWs born overseas and living in the UK during the COVID-19 pandemic. The key themes that emerged were described using Lazarus and Folkman’s transactional model of stress and coping. <b>Results:</b> The emerging themes include stressors (situation triggering stress), appraisal (situation acknowledged as a source of stress), emotion-focused coping (family and social support and religious beliefs), problem-focused coping (engaging in self-care, seeking and receiving professional support), and coping strategy outcomes. The participants described the short-term benefit of the coping strategies as a shift in focus from COVID-19, which reduced their anxiety and stress levels. However, the long-term impact is unknown. <b>Conclusion:</b> We found that some migrant HCWs struggled with their mental health and used various coping strategies during the pandemic. With an increasingly diverse healthcare workforce, it will be beneficial to explore how coping strategies (family and social support networks, religion, self-care, and professional support) could be used in the future and how occupational policies and infrastructure can be adapted to support these communities. Migrant (non-UK-born) HCWs used various coping strategies to sustain their mental health during the COVID-19 pandemic.The study conceptualized the coping mechanisms that enabled participants to cope with the COVID-19 pandemic crisis, using Lazarus and Folkman's transactional model of stress and coping.Future research should explore whether short-term gains due to coping during the pandemic were maintained in the long term. Migrant (non-UK-born) HCWs used various coping strategies to sustain their mental health during the COVID-19 pandemic. The study conceptualized the coping mechanisms that enabled participants to cope with the COVID-19 pandemic crisis, using Lazarus and Folkman's transactional model of stress and coping. Future research should explore whether short-term gains due to coping during the pandemic were maintained in the long term.
DOI: 10.6084/m9.figshare.27331246.v1
title: Coping strategies used by migrant healthcare workers to support their mental health during COVID-19 in the United Kingdom: a qualitative analysis

description: <b>Background:</b> The incidence of mental illness has risen since the coronavirus disease 2019 (COVID-19) pandemic. The number of healthcare workers (HCWs) needing mental health support has increased significantly. <b>Objective:</b> This secondary analysis of qualitative data explored the coping strategies of migrant HCWs living in the UK during the COVID-19 pandemic. Our aim was to identify the coping strategies used by migrant HCWs, and how they could be explored post-pandemic as support mechanisms of an increasingly diverse workforce. <b>Method:</b> As part of the United Kingdom Research study into Ethnicity And COVID-19 outcomes among Healthcare workers (UK-REACH), we conducted in-depth semi-structured interviews and focus groups with clinical and non-clinical HCWs across the UK, on Microsoft Teams, from December 2020 to July 2021. We conducted a thematic analysis using Braun and Clarke’s framework to explore the lived experiences of HCWs born overseas and living in the UK during the COVID-19 pandemic. The key themes that emerged were described using Lazarus and Folkman’s transactional model of stress and coping. <b>Results:</b> The emerging themes include stressors (situation triggering stress), appraisal (situation acknowledged as a source of stress), emotion-focused coping (family and social support and religious beliefs), problem-focused coping (engaging in self-care, seeking and receiving professional support), and coping strategy outcomes. The participants described the short-term benefit of the coping strategies as a shift in focus from COVID-19, which reduced their anxiety and stress levels. However, the long-term impact is unknown. <b>Conclusion:</b> We found that some migrant HCWs struggled with their mental health and used various coping strategies during the pandemic. With an increasingly diverse healthcare workforce, it will be beneficial to explore how coping strategies (family and social support networks, religion, self-care, and professional support) could be used in the future and how occupational policies and infrastructure can be adapted to support these communities. Migrant (non-UK-born) HCWs used various coping strategies to sustain their mental health during the COVID-19 pandemic.The study conceptualized the coping mechanisms that enabled participants to cope with the COVID-19 pandemic crisis, using Lazarus and Folkman's transactional model of stress and coping.Future research should explore whether short-term gains due to coping during the pandemic were maintained in the long term. Migrant (non-UK-born) HCWs used various coping strategies to sustain their mental health during the COVID-19 pandemic. The study conceptualized the coping mechanisms that enabled participants to cope with the COVID-19 pandemic crisis, using Lazarus and Folkman's transactional model of stress and coping. Future research should explore whether short-term gains due to coping during the pandemic were maintained in the long term.