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1,358 results for “Poland”
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.5. Pottery (A, C), animal bones (B), a human skull (C, D), and a flint tool (D) excavated from underneath the stone layer in Kaliszany (archaeological site no. 3)
<p>The set contains a figure, with with photographs that show examples of finds discovered during excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. <br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.1. Location of hoards mentioned in the text: white dots represent locations of hoards examined in the Biography of Hoards project; black dots represent locations of hoards examined in other multi-faceted projects
<p>The set contains a figure, with data, on the location of the hoards included (described in the related paper).<br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.3. Workflow in the Biography of Hoards project
<p>The set contains a figure and editable files associated with the figure.</p> <p>Figure presenting workflow of the project described in the related paper.</p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Survey data on people's forest use patterns and perceptions of border security measures in Białowieża Forest region, Poland
<p>A survey was conducted in June-July 2022 to obtain information about people's forest use patterns and opinions and feelings about border security measures (state of emergency, border zone closure, militarization) instituted in northeastern Poland starting in September 2021. Participants were informed that the survey was voluntary and anonymous. Participants were not obliged to respond to all questions and could stop the survey at any time. Survey completion and submission implied consent to participate. Participants had to be at least 18 years of age to take part in the survey. They had to be residents of the Białowieża Forest region. 100 persons participated in the survey. Of these 100 persons, 44 identified as local (born in the region). Data are coded and a key is provided. Some responses are aggregated and only responses to close-ended questions are shared, to prevent disclosure of potentially identifying information. </p>
Gig economy in Poland
<p>This repository contains four datasets about the number of active users of selected mobile apps purchased from Selectivv company (https://selectivv.com/). Details regarding the data may be found below:</p> <p>How data was collected: Selectivv uses programmatic advertisements systems that collect information on about 24 mln smartphone users in Poland</p> <p><strong>Apps</strong>: </p> <ul> <li>Transportation: Uber, Bolt Driver, FREE NOW, iTaxi, </li> <li>Delivery: Glover, Takeaway, Bolt Courier, Wolt; </li> </ul> <p><strong>Unit</strong>: an active user of a given app. Active = used given app at least 1 minute in a given period (e.g. 1 unit during whole month, half-year).</p> <p><strong>Period</strong>: 2018-2018; monthly and half-year data</p> <p><strong>Spatial</strong> <strong>aggregation</strong>: country level, city level, functional area level, voivodeship level. Functional area is defined as here https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/ </p> <p><strong>Activity time</strong>: measured by activity time of given app (in hours; average and standard deviation) </p> <p>Datasets:</p> <ol> <li><strong>gig-table1-monthly-counts-stats.csv</strong> -- the monthly number of active users;</li> <li><strong>gig-table2-halfyear-demo-stats.csv</strong> -- the half-year number of active users by socio-demographic variables;</li> <li><strong>gig-table3-halfyear-region-stats.csv</strong> -- the half-year number of active users by spatial aggregation;</li> <li><strong>gig-table4-halfyear-activity-stats.csv</strong> -- the half-year activity time by working week, weekend, day (8-18) and night (18-8).</li> </ol> <p>Detailed description: </p> <p><strong>1. gig-table1-monthly-counts-stats.csv</strong></p> <p>Structure: </p> <ul> <li>month - YYYY-MM-DD -- we set all dates to 15th of given month but actually the data is about the whole month (active users in whole period); 2018-01-15 to 2021-12-15</li> <li>app -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>category -- Transportation, Deliver</li> </ul> <p><strong>2. gig-table2-halfyear-demo-stats.csv </strong></p> <p>Structure: </p> <ul> <li>gender -- men, women</li> <li>age -- 18-30, 31-50, 51-64</li> <li>country -- Poland, Ukraine, Other</li> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>students -- the share of students within a given row</li> <li>parents_of_children_0_4_years -- the share of parents of 0-4 years children in a given row</li> <li>parents_of_children_5_10_years -- the share of parents of 5-10 years children in a given row</li> <li>women_planning_a_baby -- the share of women planing a baby in a given row</li> <li>standard -- the share of standard smartphones in a given row</li> <li>premium_i_phone -- the share of iPhone smartphones in a given row</li> <li>other_premium -- the share of other premium smartphones in a given row</li> <li>category -- Transportation, Delivery</li> </ul> <p><strong>3. gig-table3-halfyear-region-stats.csv</strong></p> <p>Structure:</p> <ul> <li>group -- Voivodeship, Functional Area, Cities</li> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>region_name:</li> <li>Cities -- Białystok, Bydgoszcz, Gdańsk, Gdynia, Gorzów Wielkopolski, Katowice, Kielce, Kraków, Łódź, Lublin, Olsztyn, Opole, Poznań, Rzeszów, Sopot, Szczecin, Toruń, Warszawa, Wrocław, Zielona Góra</li> <li>Functional Area -- Functional area - Białystok, Functional area - Bydgoszcz, Functional area - Gorzów Wielkopolski, Functional area - GZM, Functional area - GZM2, Functional area - Kielce, Functional area - Kraków, Functional area - Łódź, Functional area - Lublin, Functional area - Olsztyn, Functional area - Opole, Functional area - Poznań, Functional area - Rzeszów, Functional area - Szczecin, Functional area - Toruń, Functional area - Trójmiasto, Functional area - Warszawa, Functional area - Wrocław, Functional area - Zielona Góra</li> <li>Voivodeship -- dolnośląskie, kujawsko-pomorskie, łódzkie, lubelskie, lubuskie, małopolskie, mazowieckie, opolskie, podkarpackie, podlaskie, pomorskie, śląskie, świętokrzyskie, warmińsko-mazurskie, wielkopolskie, zachodniopomorskie</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>category -- Transportation, Delivery</li> </ul> <p>Please note that:</p> <ul> <li>the number of active users in a given functional area = number of active users in a city and a functional area of this city</li> <li>the number of active users in voivodeship = number of active users in a city, its functional area and the rest of the voivodeship where this city and functional area is located </li> </ul> <p>More details here: https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/ </p> <p><strong>4. gig-table4-halfyear-activity-stats.csv</strong></p> <p>Structure:</p> <ul> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>day -- Mondays-Thursdays, Fridays-Sundays</li> <li>hour -- day (8-18), night (18-8)</li> <li>activity_time -- in hours </li> <li>statistic -- Average, Std.Dev. (standard deviation)</li> <li>category -- Transportation, Delivery</li> </ul>
GNSS troposphere products from a network of low-cost GNSS receivers, Wroclaw, Poland, March-April 2021
<p>This dataset contains multi-GNSS troposphere products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties. These products were obtained using 3 processing strategies:</p> <p>1) real-time (for details see https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered);</p> <p>2) near real-time (NRT, for details see http://egvap.dmi.dk/);</p> <p>3) final (using CSRS online service, https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php).</p> <p>Products are stored as standard Matlab MAT files. Each file contains a set of table arrays (Matlab format). Each table array contains the selected set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>For convenience, the same information is stored in alternative data formats:</p> <p>1) for NRT and Final products: troposphere SINEX v1 (TRO / TRP)</p> <p>2) for real-time products: semicolon-delimited text files, with a self-explanatory header line; each file contains daily products for one station.</p>
Post-remediation evaluation of contaminated site using geophysical methods: Ortophotomosaic Olkusz (Poland) 20220629
<p>The orthophotomap is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is an orthophotomap with a 2.57 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326).</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Building locations in Poland in 1970s and 1980s
<p>Dataset contains building locations in Poland in 1970-80s. The source information were polish archival 1:10 000 topographical maps. Buildings were extracted from maps using Mask R-CNN model implemented in Esri ArcGIS Pro software. In post processing we have removed most of the false possitives. The dataset of building locations covers the entire country and contains approximately 11 million buildings. The accuracy of the dataset was assessed manually on randomly selected map sheets. The overall accuracy is 95% (F1 0.98).</p>
Quality assessment of biomass pellets available on the market: Example from Poland
<p><strong>Submitted data was used to write an article</strong>: Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., Quality assessment of biomass pellets available on the market: Example from Poland. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-024-33452-1</p> <p> </p> <p><strong>Funding acknowledgments</strong>: The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract</strong>: This study evaluates the quality of 30 biomass pellets sold for residential use in Poland. It provides data on their physical, chemical, and petrographic properties and compares them to existing standards and the information provided by the fuel producers. The results reveal considerable variations in the quality of the pellets and show that some of the purchased samples are not within the DINplus and/or ENplus certification thresholds. Among all 30 purchased samples, only one passes the quality thresholds set by the PL-US BIO, a newly established quality certification in Poland that combines quality assessment following DINplus with optical microscopy analysis. The primary issues causing a decrease in pellet quality include elevated ash and fines content, compromised mechanical durability, too low ash melting temperature, and additions of undesired additions like bark, inorganic matter, and petroleum products. Our research highlights the need for improved fuel quality control measures, and transparent and accurate product labeling, as well as the need for a comprehensive and publicly available national database of solid biomass fuel producers and fuels sold. These are essential steps toward increasing customers’ awareness and trust, encouraging them to embrace biomass fuels as reliable and sustainable sources of energy.</p> <p> </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Poland
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_PL: General Veterinary Inspectorate (GIW)</li> <li>TSE_2022_PL: General Veterinary Inspectorate (GIW)</li> <li>TSE_2021_PL: General Veterinary Inspectorate (GIW)</li> <li>TSE_2020_PL: General Veterinary Inspectorate (GIW)</li> <li>TSE_2019_PL: General Veterinary Inspectorate (GIW)</li> </ul>
Manually Annotated Drone Imagery (RGB) Dataset for automatic coastline delineation of Southern Baltic Sea, Poland with polyline annotations (0.1.1)
<p><strong>Overview:</strong></p> <p>The Manually Annotated Drone Imagery Dataset (MADRID) consists of hand annotated high resolution RGB images taken in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast in 2022-2023. All images were converted into a uniform format of 1440x2560 pixels, polyline annotated and set into file structure format suited for semantic segmentation tasks (See "Usage" notes below for more details).</p> <p>The raw images of our dataset were captured Zenmuse L1 Sensor (RGB) mounted on a DJI Matrice 300 RTK Drone. Total of 4895 images were captured, however the dataset contains 3876 images with each image annotated with coastline. The dataset only include images with coastlines that are visually identifiable with the human eye. For the annotations of the images, CVAT v2.13 open-source software was utilized.</p> <p><strong>Usage:</strong></p> <p>The compressed RAR file contains two folders train and test. Each folder contains the file that represents the date at which the image was captured in the format of (year, month, day), number of the image and the name of the drone utilized to capture the image. For example, DJI_20220111140051_0051_Zenmuse-L1-mission and DJI_20220111140105_0053_Zenmuse-L1-mission. Additionally, the test folder contains annotations (one per image) which are extracted from the original XML annotation file provided in the CVAT 1.1 image format.</p> <p>Archives were compressed using RAR compression. They can be decompressed in a terminal by opening and extracting Madrid_v0.1_Data.zip.</p> <p>The subset of the data with the name Madrid_subset_data.zip has been added which contains a small portion of train and test images for purpose of inspecting the dataset without downloading the entire dataset.</p> <p>The training images for both training data and testing data are structured as follows.</p> <pre><code>Train/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...<br><br>Test/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...</code></pre> <p> </p>
RINEX files from low-cost GNSS receivers in Wrocław, Poland; January - March, 2021
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and u-blox patch antennas (except BX02 - ArduSimple survey antenna). Time period (depending on stations): 27.02.2021 - 28.03.2021.</p>
LoGov Poland Interview Report n°7
<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Poland. To access the full transcription of this interview, the other interview reports on Poland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>
CoMix social contact data (Poland)
<p>CoMix social contact data for Poland.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Magdalena Rosinska at the National Institute of Public Health - National Institute of Hygiene.</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Poland
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <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>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
Vegetation of post-mining areas, Upper Silesia, Poland (Floristic composition of the plots_November_2022)
<p><span><span>The data set containing a list of plant species in the research plots along with their percentage coverage.</span></span> <span><span>The selection of plots took into account the occurrence of the dominant species (cover > 40% of the study plot area).</span></span> <span><span>The dominant species represent functional groups: monocots, forbs and legumes.</span></span></p>
National Checklists 2017: Poland 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 Poland collected using effechecka and geonames polygons
National Checklists 2019: Poland 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 Poland collected using effechecka and geonames polygons
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.