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Phytogeographic regions of Ukraine according to the "Flora Fungorum Ucrainicae"
<p>Origin of the data</p> <p>This regionalization was originally published by Heluta (1989), to illustrate the distribution of powdery mildew fungi across Ukraine, and further was used in the series "Flora Fungorum Ucrainicae", as well as individual publications and thesis in Mycology. The regionalization was based mainly on the current at that time Geobotanical zonation of the URSR (Barbarych et. al, 1977).<br>Since both names and accepted abbreviations of regions originally were in Russian, we adopted the translation made by Akulov et al. (2003), with some additions from a later publication by Prylutskyi & Chvikov (2020):<br>CF – Carpathian Forests, DGMS – Donetsk Gramineous-Meadow Steppe, FSCr – Forest-Steppe Crimea, KFS – Kharkiv Forest-Steppe, LFS – Left Bank Forest-Steppe, LGS – Left Bank Gramineous Steppe, LGMS – Left Bank GramineousMeadow Steppe, LP – Left Bank Polissya, MRF – Middle-Russian Forests, MCr – Mountain Crimea, PF – Precarpathian Forests, RF – Roztocze Forests, RFS – Right Bank Forest-Steppe, RGS – Right Bank Gramineous Steppe, RGMS – Right Bank Gramineous-Meadow Steppe, RP – Right Bank Polissya, SP – Small Polissya, SSCr – South Seaside of Crimea, SGMS – Starobilsk Gramineous-Meadow Steppe, SCr – Steppe Crimea, TR – Transcarpathia, VFS – Volyn Forest-Steppe, WFS – Western Forest-Steppe, WP – Western Polissya, WUF – West-Ukrainian Forests, WS – Wormwood Steppe.</p> <p><strong>UPD:</strong> Ukrainian names and abbreviations, as well as English names of the regions, updated according to <a href="https://ukrbotj.co.ua/archive/80/3/199" rel="nofollow">Heluta, 2023</a>.</p> <p>Dataset description</p> <p>Dataset (zip-archive) contains GIS vector layers with the polygons of regions, in the following formats: Geopackage, KML, and Esri shapefile. Polygons have been drawn manually using QGIS software, following verbal descriptions of the borders of regions from Heluta (1989).<br>CRS: EPSG:3857 - WGS 84 / Pseudo-Mercator<br>Charset Encoding: UTF-8</p> <p>Attribute table's fields descriptions</p> <p>fid - Unique identifier for each polygon<br>Name - Accepted abbreviated name for the region in Ukrainian<br>NameEng - Abbreviated name for the region, translated into English<br>NameFullUA - Full name of a region, in Ukrainian<br>NameFul - Full name of a region translated into English<br>NatZone - Natural zone according to the source (Heluta, 1989), in Ukrainian<br>Ecoregions - Name of the Terrestrial Ecoregion (TEOW) (Olson et al., 2001), which covers most of the area of a given region<br>Note: KML file has additional system fields, not contain attribute information.</p> <p>References</p> <p>Heluta, V.P. (2023) A critical revision of the powdery mildew fungi (Erysiphaceae, Ascomycota) of Ukraine: Erysiphe sect. Microsphaera. Ukrainian Botanical Journal. 2023. 80 (3). <a href="https://doi.org/10.15407/ukrbotj80.03.199" rel="nofollow">https://doi.org/10.15407/ukrbotj80.03.199</a></p> <p>Heluta, V.P. (1989) Powdery Mildews. Flora Fungorum Ucrainicae. Kyiv: Naukova dumka [In Russian: Гелюта, В.П. (1989) Флора грибов Украины: Мучнисторосяные грибы. Киев: Наукова думка]</p> <p>Barbarych, A.I. (Ed.) (1977)Geobotanical zonation of the URSR. Kyiv: Naukova Dumka [in Ukrainian: Геоботанічне районування Української РСР. Київ: Наукова думка]</p> <p>Akulov, O.Yu.; Usichenko, A.S.; Leontyev, D.V.; Yurchenko, E.O.; Prydiuk, M.P. (2003) Annotated checklist of aphyllophoroid fungi of Ukraine. Mycena 2:1–76.</p> <p>Chvikov, V.; Prylutskyi, О. (2020) Annotated checklist of Hygrophoraceae (Agaricales, Basidiomycota) of Ukraine. Biodivers. Ecol. Exp. Biol. 22, 6–23. https://doi.org/10.34142/2708-5848.2020.22.2.01</p> <p>Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</p>
Inventory maps of hazardous geological processes_Transcarpathia, Ukraine
<p>Under the ImProDiReT Project running at Regional Transcarpathia level an Inventory maps of the hazardous geological processes’ manifestations for the Transcarpathia (landslides, mudflows, flooding and flash floods, karst) have been created.</p>
Opinions and Views of the Population of Ukraine: May 2024 (KIIS Omnibus 2024/05) – Data from a nationwide public opinion poll conducted by KIIS in May 2024
"Opinions and Views of the Population of Ukraine" is a regular omnibus survey, conducted by Kyiv International Institute of Sociology (KIIS) among Ukraine's adult population and covering a wide range of topics. The data presented here is a subset of the survey conducted in May 2024 and include KIIS's own research questions. Questions included are: readiness for concessions for peace, views on Ukraine's relationship with Russia, perceptions of the war between Russia and Ukraine, views on security agreements, perceptions of Ukrainian society's unity, attitudes toward criticism of the government, attitudes toward the legalization of medical cannabis, and perceptions of Ukraine's statehood during the Soviet era. Data collection took place from May 16 to 22, 2024, with 1,067 respondents interviewed. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results as well as the relevant parts of the original questionnaire.
Opinions and Views of the Population of Ukraine: February 2024 (KIIS Omnibus 2024/02) – Data from a nationwide public opinion poll conducted by KIIS in February 2024
"Opinions and Views of the Population of Ukraine" is a regular omnibus survey, conducted by Kyiv International Institute of Sociology (KIIS) among Ukraine's adult population and covering a wide range of topics. The data presented here is a subset of the survey conducted in February 2024 and include KIIS's own research questions. The questions cover the following topics: readiness for concessions for peace; perceptions of Russia, its people, and leadership; sources of information; perceptions of the war between Russia and Ukraine; views on Western support for Ukraine; factors contributing to Ukraine's success in the war; perceptions of recent investigations into large businesses and businessmen in Ukraine; state control over online information; state policy on the Russian language in Ukraine; the level of democracy in Ukraine; opportunities for personal success; and favorite national holidays. Data collection took place from February 17 to 28, 2024. Some of the survey questions were asked to all respondents (n=2,008), while others were directed to a sub-sample of 1,052 respondents. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results as well as the relevant parts of the original questionnaire.
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Landslide Formation Factors Analysis for the Transcarpathia_Ukraine
<p>Spatial patterns of landslides occurrence within the Transcarpathian region using GIS tools were evaluated. In order to identify the main and derived geological factors that determine the spread and activation of landslides within the region, 2575 landslides were analyzed, with a total area of 360,576 km square. The factors were represented by clams constructed in ArcView Spatial Analyst: terrain and its derivatives (slope angles, spatial orientation of the slopes, dispersion (mean deviation) of the terrain, the trend of the terrain and its local component); density of structural-tectonic heterogeneities. It is established that the maximum of landslides development is at altitudes with gypsometric marks of 280-730 m, slopes with a slope of 7.5-22.4 °, which are oriented to the west, southwest, south and southeast and up to 500 m to watercourses. Two-thirds of all landslides investigated are within a kilometer zone along structural-tectonic disturbances and at distances of up to 1250 m of disturbances having an azimuth of 90-180°. The applied approach, for the first time made it possible to establish patterns of landslides occurrence based on the results of a large array of initial cartographic information processing (not limited by certain a priori genetic and/or theoretical interpretations) and obtain reliable limit values for characterizing landslides formation. As a result, based on mapping of areas with characteristic values of established six landslide formation factors, a landslides forecasting map was received.</p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>
Omnibus Poll Ukraine - August 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Razumkov Centre) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the sociological service of the Razumkov Center from 09 to 15 August 2023. The survey was conducted using a stratified multi-stage sample. The structure of the sample reflects the demographic structure of the adult population of the surveyed territories as of the beginning of 2022 (by age, gender, type of settlement). 2019 respondents aged 18 and older were interviewed. The theoretical sampling error does not exceed 2.3%. At the same time, additional systematic sample deviations may be caused by the consequences of Russian aggression, in particular, the forced evacuation of millions of citizens. The survey covers five thematic fields: assessment of the current situation in the country, the Russian war of aggression, energy sector, corruption, volunteering. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-file. Additionally, the data collection contains three files with "selected results" which document some major results of the survey in the form of analytical summaries and descriptive statistics: two in English, covering assessment of the current situation in the country + the Russian war of aggression as well as volunteering; one in Ukrainian covering corruption. New in version 1.1: The numbering of questions in the separate questionnaire (file "DIF_CR_0823-questionnaire-revised.pdf") has been adjusted to the numbering in the original data file ("DIF_CR_0823.sav"). A third file with "selected results" has been added. New in version 1.2: An English translation of the questionnaire has been added under "files".
Omnibus Poll Ukraine - July 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Kyiv International Institute of Sociology) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the Kyiv International Institute of Sociology from 03 to 17 July 2023. A description of the methodology is given on p.2 of the "selected results" file, which is part of this data collection. The poll covers the following thematic fields: jobs + entrepreneurship, corruption, economic situation, healthcare sector, war, people under Russian occupation. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-files. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics and another file with a clarification concerning the interpretation of question 5.24 about the president's "personal responsibility" for corruption in the country. These files are in Ukrainian only. New in version 1.1: An English translation of the questionnaire has been added under "files".
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Challenges to freedom of speech and journalists in Ukraine in times of war – Non-representative online expert survey of Ukrainian journalists (January 2023)
The expert survey of journalists was conducted from 18 to 27 January 2023 using a self-completion questionnaire in Google Forms. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation on the request of the Human Rights Centre ZMINA with the support of Freedom House Ukraine. A total of 132 people participated in the survey. The respondents were selected using the method of voluntary selection and snowballing to the point of saturation. The sample represents only the opinion of the respondents, but it also allows us to talk about certain trends and common assessments of certain phenomena and processes in the journalistic field. The survey includes questions about freedom of speech and self-censorship in the media environment during the Russian-Ukrainian war. The data collection contains original survey data. The Excel file (.xlsx) is the original file with the respondents' answers in Ukrainian, provided by the Ilko Kucheriv Democratic Initiatives Foundation. The documentation includes the questions and answer options of the original questionnaire in Ukrainian and English. Additionally, the data collection contains the "Summary" file, which is an analytical report prepared by the Ilko Kucheriv Democratic Initiatives Foundation and the Human Rights Centre ZMINA. The report uses data from an expert survey of journalists in 2019 and 2023, and the results of focus groups in 2022.
Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way
<p>This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way".</p> <p>The results of the main five scenarios of the study are available as PyPSA result files:</p> <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> <p>See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>.</p> <p>The results of the 330 global sensitivity analysis runs are available as summary files in CSV format:</p> <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>
National Checklists 2017: Ukraine Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Ukraine collected using effechecka and geonames polygons
National Checklists 2019: Ukraine Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Ukraine collected using effechecka and geonames polygons
Protests Ukraine Covid 2020-22: YouTube Videos
The collection "YTV Protests Ukraine Covid 2020-22" contains 146 videos (mp4) on protests relating to government measures due to Covid-19. We have downloaded all data in October 2024 and made screenshots (pdf) of websites so that the discussion and comments on the single video posts can be followed. All data is processed in an MS Excel database with metadata. We collect all videos that are 1) event related, 2) show actions of this event, 3) we can find with our search words during a particular period. We strictly aim at a systematic and objective selection and organized storage of protest-related videos. The collection is based on extensive research into Covid-related protest events in Ukraine, which made it possible to identify relevant search words. According to the snowball principle, we then start the collection of videos with the help of these search words and try to download as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached. Due to copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. The videos have been posted mainly by TV channels and news outlets. Therefore, the material is only an extract and biased by the perspective of the single creator/creating institution. The collection is part of a larger and ongoing collection of videos on protest events in the post-Soviet region.
The Invasion of Ukraine Viewed through TikTok: A Dataset
<p>This is a dataset of videos and comments related to the invasion of Ukraine, published on TikTok by a number of users over the year of 2022. It was compiled by Benjamin Steel, Sara Parker and Derek Ruths at the Network Dynamics Lab, McGill University. We created this dataset to facilitate the study of TikTok, and the nature of social interaction on the platform relevant to a major political event.</p> <p>The dataset has been released here on Zenodo: <a href="https://doi.org/10.5281/zenodo.7534952">https://doi.org/10.5281/zenodo.7926959</a> as well as on Github: <a href="https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok">https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok</a></p> <p>To create the dataset, we identified hashtags and keywords explicitly related to the conflict to collect a core set of videos (or ”TikToks”). We then compiled comments associated with these videos. All of the data captured is publically available information, and contains personally identifiable information. In total we collected approximately 16 thousand videos and 12 million comments, from approximately 6 million users. There are approximately 1.9 comments on average per user captured, and 1.5 videos per user who posted a video. The author personally collected this data using the web scraping PyTok library, developed by the author: <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a>.</p> <p>Due to scraping duration, this is just a sample of the publically available discourse concerning the invasion of Ukraine on TikTok. Due to the fuzzy search functionality of the TikTok, the dataset contains videos with a range of relatedness to the invasion.</p> <p>We release here the unique video IDs of the dataset in a CSV format. The data was collected without the specific consent of the content creators, so we have released only the data required to re-create it, to allow users to delete content from TikTok and be removed from the dataset if they wish. Contained in this repository are scripts that will automatically pull the full dataset, which will take the form of JSON files organised into a folder for each video. The JSON files are the entirety of the data returned by the TikTok API. We include a script to parse the JSON files into CSV files with the most commonly used data. We plan to further expand this dataset as collection processes progress and the war continues. We will version the dataset to ensure reproducibility.</p> <p>To build this dataset from the IDs here:</p> <ol> <li>Go to <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a> and clone the repo locally</li> <li>Run <code>pip install -e .</code> in the pytok directory</li> <li>Run <code>pip install pandas tqdm</code> to install these libraries if not already installed</li> <li>Run <code>get_videos.py</code> to get the video data</li> <li>Run <code>video_comments.py</code> to get the comment data</li> <li>Run <code>user_tiktoks.py</code> to get the video history of the users</li> <li>Run <code>hashtag_tiktoks.py</code> or <code>search_tiktoks.py</code> to get more videos from other hashtags and search terms</li> <li>Run <code>load_json_to_csv.py</code> to compile the JSON files into two CSV files, <code>comments.csv</code> and <code>videos.csv</code></li> </ol> <p>If you get an error about the wrong chrome version, use the command line argument <code>get_videos.py --chrome-version YOUR_CHROME_VERSION</code> Please note pulling data from TikTok takes a while! We recommend leaving the scripts running on a server for a while for them to finish downloading everything. Feel free to play around with the delay constants to either speed up the process or avoid TikTok rate limiting.</p> <p>Please do not hesitate to make an issue in this repo to get our help with this!</p> <p> </p> <p>The <code>videos.csv</code> will contain the following columns:</p> <p><code>video_id</code>: Unique video ID</p> <p><code>createtime</code>: UTC datetime of video creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>desc</code>: The full video description from the author</p> <p><code>hashtags</code>: A list of hashtags used in the video description</p> <p><code>share_video_id</code>: If the video is sharing another video, this is the video ID of that original video, else empty</p> <p><code>share_video_user_id</code>: If the video is sharing another video, this the user ID of the author of that video, else empty</p> <p><code>share_video_user_name</code>: If the video is sharing another video, this is the user name of the author of that video, else empty</p> <p><code>share_type</code>: If the video is sharing another video, this is the type of the share, stitch, duet etc.</p> <p><code>mentions</code>: A list of users mentioned in the video description, if any</p> <p> </p> <p>The <code>comments.csv</code> will contain the following columns:</p> <p><code>comment_id</code>: Unique comment ID</p> <p><code>createtime</code>: UTC datetime of comment creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>text</code>: Text of the comment</p> <p><code>mentions</code>: A list of users that are tagged in the comment</p> <p><code>video_id</code>: The ID of the video the comment is on</p> <p><code>comment_language</code>: The language of the comment, as predicted by the TikTok API</p> <p><code>reply_comment_id</code>: If the comment is replying to another comment, this is the ID of that comment</p> <p>The date can be compiled into a user interaction network to facilitate study of interaction dynamics. There is code to help with that here: <a href="https://github.com/networkdynamics/polar-seeds">https://github.com/networkdynamics/polar-seeds</a>. Additional scripts for further preprocessing of this data can be found there too.</p>
Ukraine Fire Perimeters 2022
<p>The data set contains the fire perimeters for Ukraine for 2022 sourced from remote sensing data. The locations and dates of burning were detected using MODIS/VIIRS products on thermal anomalies. This information was used to select pre- and post-fire Copernicus Sentinel 2 L2A imagery. Satellite images were screened from clouds, cloud shadows and combined into median composite mosaics. The image mosaics were created using all available imagery within a 14-day time window before (pre-fire mosaic) and after (post-fire mosaic) each fire. Fire perimeters were visually delineated by comparing post and pre-fire image mosaics using a SWIR2–NIR–Red band combination. The spatial accuracy of fire perimeters corresponds to Sentinel 2 data at 20-m spatial resolution.</p> <p>Delineated fire perimeters were intersected with the Copernicus Dynamic Land Cover map at 100 m resolution (v.3.0.1) to extract burned areas of five land cover classes according to the following reclassification scheme of the original pixel values.</p> <table> <tbody> <tr> <td> <p><strong>Land cover class</strong></p> </td> <td> <p><strong>Original pixel values</strong></p> </td> </tr> <tr> <td> <p>Coniferous forest (LC_1)</p> </td> <td> <p>111</p> </td> </tr> <tr> <td> <p>Broadleaved forest (LC_2)</p> </td> <td> <p>112,113,114,115,116</p> </td> </tr> <tr> <td> <p>Other natural landscape (LC_3)</p> </td> <td> <p>121,124,125,126, 20,30,60,90</p> </td> </tr> <tr> <td> <p>Agricultural land (LC_4)</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>Settlement (LC_5)</p> </td> <td> <p>50</p> </td> </tr> </tbody> </table> <p><strong>MetaData</strong></p> <table> <tbody> <tr> <td> <p>Field name</p> </td> <td>Description</td> </tr> <tr> <td>id</td> <td>Unique identifier of each fire perimeter</td> </tr> <tr> <td>longitude</td> <td>Longitude referring to centroids of fire perimeters</td> </tr> <tr> <td>latitude</td> <td>Latitude referring to centroids of fire perimeters</td> </tr> <tr> <td>date</td> <td>Date of burning in the format DD.MM.YYYY</td> </tr> <tr> <td>year</td> <td>Year</td> </tr> <tr> <td>month</td> <td>Month</td> </tr> <tr> <td>day</td> <td>Day of the month</td> </tr> <tr> <td>week</td> <td>Week number in the year</td> </tr> <tr> <td>reg</td> <td>The capital city name of the Ukrainian regions (oblast) associated with each fire</td> </tr> <tr> <td>occupied</td> <td>A binary indicator variable referring to the locations of each fire within the Russian-occupied territory (1) or within the territory controlled by the Government of Ukraine (0) for a given date</td> </tr> <tr> <td>buff_30km</td> <td>A binary indicator variable referring to the location of each fire within a 30-km buffer zone on both sides of the front line. In contrast to a daily progression front line used to detect "occupied" territory, the buffer was calculated using the farthest position of a frontline towards territories controlled by the Government of Ukraine</td> </tr> <tr> <td>emerald</td> <td>A binary indicator variable referring to locations of each fire within the Emerald network</td> </tr> </tbody> </table> <p> Burned areas by land cover (LC) type (in hectares) within the fire perimeter according to the Copernicus Dynamic Land Cover map at 100 m resolution (v.3.0.1)</p> <table> <tbody> <tr> <td>LC_0</td> <td>N/A</td> </tr> <tr> <td>LC_1</td> <td>Coniferous forest</td> </tr> <tr> <td>LC_2</td> <td>Broadleaved forest </td> </tr> <tr> <td>LC_3</td> <td>Other natural landscape</td> </tr> <tr> <td>LC_4</td> <td>Agricultural land </td> </tr> <tr> <td>LC_5</td> <td>Settlement</td> </tr> </tbody> </table>
Hazardous geological processes occurrence assessment for Transcarpathian region,_Ukraine
<p>Maps of hazardous geological processes specific occurrence by administrative districts for Transcarpathian region were produced by the Institute of Geological Sciences of the National Academy of Sciences of Ukraine based on the processing of materials from such institutions: State Service of Geology and Mineral Resources of Ukraine, Transcarpathian geological and hydrogeological center of the State Enterprise "Zakhidukrgeologiia" of the National Joint Stock Company "Nadra Ukrainy", Berehovo, State Geological Information Archive of Ukraine. In particular, maps of the distribution of hazardous geological processes with a scale of 1:100000 (by V. Barnychka, 1980) and a scale of 1: 200000 (by M. Gabor) for the period 1980-2010 were used, as well as data provided by V. Petryk ("Zakhidukrgeologiia", 1983-2001), and data from information yearbooks on the of hazardous exogenous geological processes activization for Ukraine territory according to monitoring of engineering and geological processes 2015-2018. The ranking principles for Transcarpathian region administrative districts due to the hazardous geological processes occurrence depended on type of process.</p>
Figure 3 in A new species of Incestophantes Tanasevitch, 1992 (Araneae, Linyphiidae) from Ukraine
Figure 3. Incestophantes australis sp. n., epigyne: a ventral aspect b caudal aspect c dorsal aspect d vulva. I. crucifer, epigyne: e ventral aspect f caudal aspect g aspect h vulva. Scale bars = 0.1 mm.
Figure1 in A new species of Incestophantes Tanasevitch, 1992 (Araneae, Linyphiidae) from Ukraine
Figure1. Distribution of Incestophantes species in Ukraine: filled star – I. australis; filled circle – I. crucifer.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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OpenNeuro
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