Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Hungary

This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001. Reporting authorities contributing to each data collection: <ul> <li>TSE_2023_HU: National Food Chain Safety Office (NFCSO)</li> <li>TSE_2022_HU: National Food Chain Safety Office (NFCSO)</li> <li>TSE_2021_HU: National Food Chain Safety Office (NFCSO)</li> <li>TSE_2020_HU: National Food Chain Safety Office (NFCSO)</li> <li>TSE_2019_HU: National Food Chain Safety Office (NFCSO)</li> </ul>

opencc-by-4.0Sep 2022View details →

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Hungary

This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of: <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>

opencc-by-sa-4.0May 2024View details →

Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland

Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).  The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.  In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education. Finally, groups were same-sex moderated. Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised.  FG_CHE_1 Female     Female Group, Switzerland FG_CHE_2 Male Male Group, Switzerland     FG_DEN_1 Female     Female Groups, Denmakr FG_DEN_2 Male Male Group, Denmark     FG_DEN_3 Male     Male Group, Denmark FG_DEN_4 Mixed     Mix Male and Female Group, Switzerland FG_ESP_1 Male Male Group, Spain FG_ESP_2 Male Male Group, Spain     FG_ESP_3 Female     Female Group, Spain FG_ESP_4 Female Female Group, Spain FG_GBR_1 Female     Female Group, UK FG_GBR_2 Male     Male Group, UK FG_GER_1 Female     Female Group, Germany FG_GER_2 Male Male Group, Germany FG_HUN_1 Female     Female Group, Hungary FG_HUN_2 Female     Female Group, Hungary FG_HUN_3 Male Male Group, Hungary     FG_HUN_4 Male Male Group, Hungary

opencc-by-4.0Sep 2016View details →

Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)

Dataset used to perform the following manuscripts:  - Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. JMIR mHealth and uHealth, 4(3), e112. http://doi.org/10.2196/mhealth.5716 rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework.

opencc-zeroAug 2024View details →

National Checklists 2017: Hungary Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Hungary collected using effechecka and geonames polygons

opencc-by-4.0Aug 2024View details →

Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000

Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils. For more details / to cite this dataset please use: <a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a> Custom code used for digital soil mapping and validation is available on GitHub: <a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a> Description of the files: The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps. <table> <tbody> <tr> <td> SOC property maps </td> <td> Unit </td> <td> Year </td> <td> Filename </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q95.tif </td> </tr> </tbody> </table>

opencc-zeroAug 2024View details →

National Checklists 2019: Hungary Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Hungary collected using effechecka and geonames polygons

opencc-by-4.0Aug 2024View details →

Decomposition data in organic, permaculture and conventional horticultural farms of Central Hungary

opencc-by-4.0Sep 2024View details →

Labor flows and input-output connections in Hungary

Aggregated data on labor flows and input-output connections in Hungary (2015-2017). Related preprint: https://arxiv.org/abs/2405.07071 Interactive visualization site: https://vis.csh.ac.at/colocation-suppliers/ The related research by Sándor Juhász was supported by the European Union’s Marie Sklodowska-Curie Postdoctoral Fellowship Program (SUPPED, grant number 101062606). The data preparation was done with the help of the Databank of HUN-REN Centre for Economic and Regional Studies. The shared datasets are based on the value-added tax return data files of the Hungarian Central Statistical Office. The calculations and conclusions drawn from them are the sole intellectual property of the authors.

Figures 7-9 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 7-9. Parasyrisca arrabonica Szinetár & Eichardt, sp. n. 7 male pedipalp, ventral view 8 same, semi-retrolateral view, showing the pointed tip of the conductor (c – arrowed) 9 same, retrolateral view. Scale bar = 0.3.

Figures 12-14 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 12-14. Parasyrisca arrabonica Szinetár & Eichardt, sp. n., male habitus: 12 paratype from Orgovány, prosoma, dorsolateral view, showing the strong setae on the paturon 13 same specimen, dorsal view 14 same specimen, ventral view. Scale bar = 2.0.

Figures 1-6 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 1-6. Parasyrisca arrabonica Szinetár & Eichardt, sp. n.: Male holotype: 1 pedipalp, prolateral view 2 same, ventral view 3 same, retrolateral view. Female: 4 epigyne, ventral view 5 vulva, dorsal view 6 posterior ridge (PRE) of the female epigyne, rear view. Scale bars: 1-3 0.3 4-6 0.2.

Figures 18-19 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 18-19. Distribution maps of the genus Parasyrisca: 18 Eurasian distribution of the species groups of Parasyrisca (red, pink – potanini group, pink – P. arrabonica, P. turkenica, P. songi, yellow – vinosus group, light blue – guzeripli group, dark blue – breviceps group) 19 European distribution of Parasyrisca (red – potanini group, yellow – vinosus group). Yellow question marks represent doubtful records.

opencc-by-4.0Dec 2020View details →

AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Hungary

This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of: <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>

opencc-zeroDec 2016View details →

FIGURES 2 – 3 in A new Aceria species (Acari: Prostigmata: Eriophyoidea) from Minuartia frutescens (Caryophyllaceae) in Hungary

FIGURES 2 – 3. SEM micrographs of Aceria wassalberti n. sp. (2) anterodorsal view of probably a female and (3) ventrolateral view of anterior region of male.

opencc-by-4.0Aug 2022View details →

Results from national testing programs on the occurrence of chemical contaminants in food and feed - Hungary

In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.   The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food.  Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below:   <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’).  More details are available in last year's finalised call for data ‘<a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a>’. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:  OCC-CHEMMON2020 – National Food Chain Safety Office OCC-CHEMMON2021 – National Food Chain Safety Office OCC-CHEMMON2022 – National Food Chain Safety Office OCC-CHEMMON2023 – National Food Chain Safety Office OCC-CHEMMON2024 – National Food Chain Safety Office

opencc-by-4.0Aug 2022View details →

Text-fig. 1. Situation of Early Miocene plant localities. a: Central Europe with Brno area and other fossil sites mentioned in text (1 – Znojmo and Přímětice, 2 – Oberdorf, 3 – Modrý Kameň Basin, 4 – Lipovany, 5 – Ipolytarnóc; CZ – the Czech Republic, PL – Poland, SK – Slovakia, H – Hungary, A – Austria, D – Germany). b: Brno area with Líšeň municipal district indicated. c: Líšeň municipal district with fossil sites indicated by asterisk. in A New Early Miocene (Ottnangian) Flora Of The "Rzehakia Beds" From Brno-Líšeň

Text-fig. 1. Situation of Early Miocene plant localities. a: Central Europe with Brno area and other fossil sites mentioned in text (1 – Znojmo and Přímětice, 2 – Oberdorf, 3 – Modrý Kameň Basin, 4 – Lipovany, 5 – Ipolytarnóc; CZ – the Czech Republic, PL – Poland, SK – Slovakia, H – Hungary, A – Austria, D – Germany). b: Brno area with Líšeň municipal district indicated. c: Líšeň municipal district with fossil sites indicated by asterisk.

opencc-by-4.0Aug 2023View details →

Online trust in Information Society. Four representative database and questionnaire (Hungary, Romania, Poland, Czech Republic

This data collection was conducted by the Institute of the Information Society of the University of Public Service - Ludovika, and covered four Central European countries: the Czech Republic, Hungary, Poland, and Romania, with the aim of examining the characteristics of the use of information technology by the adult population in the region, mainly for communication purposes. A telephone survey was conducted in October and November 2019, and the results are representative of the population over 18 years of age in the four countries, categorized by age, gender, education, type of settlement and region. The data set contains: <ul> <li>Questionnaires in the original languages (Hungarian, Czech, Romanian and Polish), and all questionnaires in English language, too.</li> <li>The four databases with variables in English</li> <li>Merged database of four databases</li> <li>Code table about variables </li> </ul>