cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c52z49
title: MassIVE MSV000087544 - Envenomation by Bothrops leucurus
title: Bothrops spp. is responsible for about 70% of snakebites, causing a diverse and complex pathophysiology, ranging from local to systemic effects in Brazil. Bothrops leucurus is the most common species in the Atlantic range of the Northeast and the main species of medical relevance in the region. Although the influence of the variability of Bothrops venom on the pathology, and the importance of B. leucurus is recognized, the pathophysiological effects involved in snakebite by this species, as well as the reaction of organisms in response to the envenoming have not been explored. We identify the plasma proteins that are affected in terms of abundance during envenoming, and their reflection in the face of biological and pathophysiological processes associated with the local tissue damage caused by snake venom B. leucurus.
DOI: 10.5281/zenodo.4748652
title: Caregivers of people with dementia and mental health during COVID-19: findings from a cross-sectional study.

description: The dataset was generated during a cross sectional survey conducted in May-June 2020, aimed at exploring the impact of COVID 19 pandemic on mental health of caregivers of people with dementia. Data come from a sample of 571 caregivers of people with dementia living in Italy and Southern Switzerland. We registered socio-demographic characteristics, and information about the relationship with the care recipient, dementia subtype, care inputs from others, and the need of care of the person with dementia. We measured caregiver burden with the Zarit Burden Interview (ZBI), psychological distress with the Depression, Anxiety and Stress Scale (DASS-21), and perceived isolation with the 3-item UCLA Loneliness Scale (UCLALS3).
DOI: 10.5281/zenodo.4785240
title: Dataset for Modeling and experimental study of the effect of pore water velocity on the spectral induced polarization signature in porous media

description: Dataset for the paper: Modeling and experimental study of the effect of pore water velocity on the spectral induced polarization signature in porous media
DOI: 10.5281/zenodo.4785239
title: Dataset for Modeling and experimental study of the effect of pore water velocity on the spectral induced polarization signature in porous media

description: Dataset for the paper: Modeling and experimental study of the effect of pore water velocity on the spectral induced polarization signature in porous media
DOI: 10.5281/zenodo.4872489
title: Tree bark identification dataset (BARK-KR)

description: Tree bark identification dataset containing 6,918 raw images of total 54 tree species. The pre-processed dataset utilized in the publications will be uploaded soon (currently available on request). The images were captured in parks and forests near Seoul, Republic of Korea. The list of species are as follows: <em>- Abies holophylla<br> - Abies nephrolepis<br> - Acer palmatum<br> - Acer palmatum var. amoenum<br> - Acer pictum subsp. mono<br> - Acer pseudosieboldianum<br> - Aesculus turbinata<br> - Alnus hirsut<br> - Alnus japonica<br> - Berchemia berchemiifolia<br> - Betula platyphylla var. japonica<br> - Carpinus laxiflora<br> - Carpinus tschonoskii<br> - Castanea crenata<br> - Cercidiphyllum japonicum<br> - Chamaecyparis pisifera<br> - Chionanthus retusus<br> - Cornus controversa<br> - Cornus kousa<br> - Fraxinus rhynchophylla<br> - Ginkgo biloba<br> - Hovenia dulcis<br> - Kalopanax septemlobus<br> - Maackia amurensis<br> - Magnolia denudata<br> - Magnolia obovata<br> - Metasequoia glyptostroboides<br> - Morus bombycis<br> - Pinus densiflora<br> - Pinus koraiensis<br> - Pinus rigida<br> - Pinus strobus<br> - Pinus × rigitaeda<br> - Platanus occidentalis<br> - Populus Tomentiglandulosa<br> - Prunus serrulata<br> - Prunus × yedoensis<br> - Quercus acutissima<br> - Quercus alba<br> - Quercus aliena<br> - Quercus dentata<br> - Quercus mongolica<br> - Quercus serrata<br> - Quercus variabilis<br> - Quercus × urticifolia<br> - Robinia pseudoacacia<br> - Sorbus alnifolia<br> - Stewartia pseudocamellia<br> - Styphnolobium japonicum<br> - Styrax japonicus<br> - Taxodium distichum<br> - Tilia amurensis<br> - Ulmus parvifolia<br> - Zelkova serrata</em>
description: This work was supported by Korea Forest Service (Korea Forestry Promotion Institute), grant number 2018113B10-2020-BB01 and 2020185D10-2122-AA02.
DOI: 10.5281/zenodo.4749062
title: Tree bark identification dataset (BARK-KR)

description: Tree bark identification dataset containing 6,918 raw images of total 54 tree species. The pre-processed dataset utilized in the publications will be uploaded soon (currently available on request). The images were captured in parks and forests near Seoul, Republic of Korea. The list of species are as follows: <em>- Abies holophylla<br> - Abies nephrolepis<br> - Acer palmatum<br> - Acer palmatum var. amoenum<br> - Acer pictum subsp. mono<br> - Acer pseudosieboldianum<br> - Aesculus turbinata<br> - Alnus hirsut<br> - Alnus japonica<br> - Berchemia berchemiifolia<br> - Betula platyphylla var. japonica<br> - Carpinus laxiflora<br> - Carpinus tschonoskii<br> - Castanea crenata<br> - Cercidiphyllum japonicum<br> - Chamaecyparis pisifera<br> - Chionanthus retusus<br> - Cornus controversa<br> - Cornus kousa<br> - Fraxinus rhynchophylla<br> - Ginkgo biloba<br> - Hovenia dulcis<br> - Kalopanax septemlobus<br> - Maackia amurensis<br> - Magnolia denudata<br> - Magnolia obovata<br> - Metasequoia glyptostroboides<br> - Morus bombycis<br> - Pinus densiflora<br> - Pinus koraiensis<br> - Pinus rigida<br> - Pinus strobus<br> - Pinus × rigitaeda<br> - Platanus occidentalis<br> - Populus Tomentiglandulosa<br> - Prunus serrulata<br> - Prunus × yedoensis<br> - Quercus acutissima<br> - Quercus alba<br> - Quercus aliena<br> - Quercus dentata<br> - Quercus mongolica<br> - Quercus serrata<br> - Quercus variabilis<br> - Quercus × urticifolia<br> - Robinia pseudoacacia<br> - Sorbus alnifolia<br> - Stewartia pseudocamellia<br> - Styphnolobium japonicum<br> - Styrax japonicus<br> - Taxodium distichum<br> - Tilia amurensis<br> - Ulmus parvifolia<br> - Zelkova serrata</em>
description: This work was supported by Korea Forest Service (Korea Forestry Promotion Institute), grant number 2018113B10-2020-BB01 and 2020185D10-2122-AA02.
DOI: 10.6084/m9.figshare.14703648
title: H3K27Ac diffpeak Zv9

description: For UCSC zv9m assembly
DOI: 10.6084/m9.figshare.14703648.v1
title: H3K27Ac diffpeak Zv9

description: For UCSC zv9m assembly
DOI: 10.5281/zenodo.4876458
title: nnDetection Decathlon Labels

description: Labels used for nnDetection V0.1. More info can be found here.<br> The labels are derived from the Medical Segmentation Decathlon and usage needs to comply with the original license.
DOI: 10.5281/zenodo.4749063
title: Tree bark identification dataset (BARK-KR)

description: <strong>This version is deprecated and possesses several misclassified samples. Please refer to the updated version at: https://doi.org/10.5281/zenodo.4872489</strong> Tree bark identification dataset containing 7,139 images of total 54 tree species. The images were captured in parks and forests near Seoul, Republic of Korea. The list of species are as follows: <em>- Abies holophylla<br> - Abies nephrolepis<br> - Acer palmatum<br> - Acer palmatum var. amoenum<br> - Acer pictum subsp. mono<br> - Acer pseudosieboldianum<br> - Aesculus turbinata<br> - Alnus hirsut<br> - Alnus japonica<br> - Berchemia berchemiifolia<br> - Betula platyphylla var. japonica<br> - Carpinus laxiflora<br> - Carpinus tschonoskii<br> - Castanea crenata<br> - Cercidiphyllum japonicum<br> - Chamaecyparis pisifera<br> - Chionanthus retusus<br> - Cornus controversa<br> - Cornus kousa<br> - Fraxinus rhynchophylla<br> - Ginkgo biloba<br> - Hovenia dulcis<br> - Kalopanax septemlobus<br> - Maackia amurensis<br> - Magnolia denudata<br> - Magnolia obovata<br> - Metasequoia glyptostroboides<br> - Morus bombycis<br> - Pinus densiflora<br> - Pinus koraiensis<br> - Pinus rigida<br> - Pinus strobus<br> - Pinus × rigitaeda<br> - Platanus occidentalis<br> - Populus Tomentiglandulosa<br> - Prunus serrulata<br> - Prunus × yedoensis<br> - Quercus acutissima<br> - Quercus alba<br> - Quercus aliena<br> - Quercus dentata<br> - Quercus mongolica<br> - Quercus serrata<br> - Quercus variabilis<br> - Quercus × urticifolia<br> - Robinia pseudoacacia<br> - Sorbus alnifolia<br> - Stewartia pseudocamellia<br> - Styphnolobium japonicum<br> - Styrax japonicus<br> - Taxodium distichum<br> - Tilia amurensis<br> - Ulmus parvifolia<br> - Zelkova serrata</em>
description: This work was supported by Korea Forest Service (Korea Forestry Promotion Institute), grant number 2018113B10-2020-BB01 and 2020185D10-2122-AA02.
DOI: 10.5281/zenodo.4876474
title: Paediatric surgical trials, their fragility index, and why to avoid using it to evaluate trials

description: Dataset for the evaluation of fragility indices in paediatric surgical trials.
DOI: 10.5281/zenodo.4876472
title: nnDetection KiTS19 Labels

description: Labels used for nnDetection V0.1. More info can be found here.<br> The labels are derived from the original KiTS19 Segmentation Challenge and usage needs to comply with the original license.<br> Some of the cases were excluded from training and evaluation, the clusters were built via connected component analysis and the clusters of cases 42, 46, 145 were manually corrected.
DOI: 10.5281/zenodo.4876471
title: nnDetection KiTS19 Labels

description: Labels used for nnDetection V0.1. More info can be found here.<br> The labels are derived from the original KiTS19 Segmentation Challenge and usage needs to comply with the original license.<br> Some of the cases were excluded from training and evaluation, the clusters were built via connected component analysis and the clusters of cases 42, 46, 145 were manually corrected.
DOI: 10.5281/zenodo.4876497
title: nnDetection Decathlon Labels

description: Labels used for nnDetection V0.1. More info can be found here.<br> The labels are derived from the Medical Segmentation Decathlon and usage needs to comply with the original license.
description: Remove Mac OS specific files.
DOI: 10.5281/zenodo.4876457
title: nnDetection Decathlon Labels

description: Labels used for nnDetection V0.1. More info can be found here.<br> The labels are derived from the Medical Segmentation Decathlon and usage needs to comply with the original license.
description: Remove Mac OS specific files.
DOI: 10.34808/wznj-fr03
title: The aggregation of objects representing Gdańsk district buildings - scale 1:10000. Data from OSM.

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The data include the spatial scope of Gdańsk district. Most buildings do not have a prescribed type. The provided data include original data downloaded from OSM (on 26 April 2021) [1] as well as processed data. The aggregation process was performed taking into account the proximity of the neighborhood [2] based on the minimum size of the figure [3, 4]. The data are the basis for the subsequent steps in the process of sequential generalization of building groups.
Literature
[1] http://download.geofabrik.de/ [access: 26.04.2021].
[2] Kozioł, Krystian, and Michał Lupa. "The use of merging and aggregation operators for MRDB data feeding." Geoinformatica Polonica 12 (2013): 17-24.
[3] Chrobak, Tadeusz. Badanie przydatności trójkąta elementarnego w komputerowej generalizacji kartograficznej. Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, 1999.
[4] Chrobak, Tadeusz, et al. "A method for assessing generalized data accuracy with linear object resolution verification." Geocarto International 32.3 (2017): 238-256.
DOI: 10.34808/r9bw-d904
title: The generalization by simplification operator with Chrobak’s method of objects representing groups of buildings in Gdańsk district - scale 1:10000. Data from OSM

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (GDA_10k_OSM_BuildingAggregation). The Chrobak's operator was designed to simplify lines [2]. For the purpose of simplifying buildings, this method has been adapted and extended by verifying the minimum size of the object using the cartographic matrix [3]. The resulting objects require another stage - orthogonalization.
Literature
[1] www.openstreetmap.org
[2] Chrobak, T. "Metoda uogólnienia danych w procesie generalizacji obiektów liniowych." Archiwum Fotogrametrii, Kartografii i Teledetekcji 13 (2003).
[3] Chrobak, Tadeusz. "Przydatność osnowy kartograficznej i metody obiektywnego upraszczania obiektów do aktualizacji danych w BDT." Geomatics and Environmental Engineering 3 (2009): 81-90.
DOI: 10.5281/zenodo.4876499
title: C2 - PTW - TRACK - Biometric features

description: Biometric features of all the tasks (depending on the subject) computed from the available physiological signals.
description: Recorded data with poor signal-to-noise ratio, the performed analysis are not reliable.
DOI: 10.5281/zenodo.4876501
title: C2 - Pedestrian - SIMULATOR - Biometric features

description: Biometric features of all the tasks (depending on the subject) computed from the available physiological signals.
description: Recorded data with poor signal-to-noise ratio, the performed analysis are not reliable.
DOI: 10.5281/zenodo.4876496
title: C2 - PTW - SIMULATOR - Biometric features

description: Biometric features of all the tasks (depending on the subject) computed from the available physiological signals.
DOI: 10.5281/zenodo.4876492
title: C2 - CAR - TRACK - Biometric features

description: Biometric features of all the tasks (depending on the subject) computed from the available physiological signals.
DOI: 10.5281/zenodo.4876478
title: C2 - CAR - SIMULATOR - Biometric features

description: Biometric features of all the tasks (depending on the subject) computed from the available physiological signals.
DOI: 10.34808/20mn-3n56
title: The generalization by simplification operator with Sester’s method of objects representing groups of buildings in Gdańsk district - scale 1:10000. Data from OSM

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (GDA_10k_OSM_BuildingAggregation). The Sester's simplifying operator is designed to simplify buildings, eliminating sections that do not meet the minimum length [2, 3].
Literature
[1] www.openstreetmap.org
[2] Sester, Monika, and Claus Brenner. "Continuous generalization for fast and smooth visualization on small displays." International Archives of Photogrammetry and Remote Sensing 34.part 4 (2004): 1293-1298.
[3] Sester, Monika, and Claus Brenner. "A vocabulary for a multiscale process description for fast transmission and continuous visualization of spatial data." Computers & Geosciences 35.11 (2009): 2177-2184.
DOI: 10.34808/kwsa-z439
title: The generalization by simplification operator with the Simplify Building tool of objects representing groups of buildings in Gdańsk district - scale 1:10000. Data from OSM

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (GDA_10k_OSM_BuildingAggregation). The employed Simplify Building tool is described by the manufacturer Esri Inc. in the documentation [2]. As a result of the simplification process, several building groups have been removed.
Literature
[1] www.openstreetmap.org
[2] www.pro.arcgis.com [access: 15.11.2020]
DOI: 10.6084/m9.figshare.14703657.v1
title: H3K27Ac LEN diffpeak ZCAT_CTRL

description: For UCSC
DOI: 10.6084/m9.figshare.14703657
title: H3K27Ac LEN diffpeak ZCAT_CTRL

description: For UCSC
DOI: 10.34808/bbpe-1z55
title: The generalization by simplification operator with Chrobak’s method of objects representing groups of buildings in Kartuzy district - scale 1:10000. Data from OSM.

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_10k_OSM _BuildingAggregation). The Chrobak's operator was designed to simplify lines [2]. For the purpose of simplifying buildings, this method has been adapted and extended by verifying the minimum size of the object using the cartographic matrix [3].
Literature
[1] http://download.geofabrik.de/ [access: 26.04.2021].
[2] Chrobak, T. "Metoda uogólnienia danych w procesie generalizacji obiektów liniowych." Archiwum Fotogrametrii, Kartografii i Teledetekcji 13 (2003).
[3] Chrobak, Tadeusz. "Przydatność osnowy kartograficznej i metody obiektywnego upraszczania obiektów do aktualizacji danych w BDT." Geomatics and Environmental Engineering 3 (2009): 81-90.
DOI: 10.34808/h88t-1060
title: The generalization by simplification operator with Sester’s method of objects representing groups of buildings in Kartuzy district - scale 1:10000. Data from OSM

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_10k_OSM _BuildingAggregation). The Sester's simplifying operator is designed to simplify buildings, eliminating sections that do not meet the minimum length [2, 3].
Literature
[1] http://download.geofabrik.de/ [access: 26.04.2021].
[2] Sester, Monika, and Claus Brenner. "Continuous generalization for fast and smooth visualization on small displays." International Archives of Photogrammetry and Remote Sensing 34.part 4 (2004): 1293-1298.
[3] Sester, Monika, and Claus Brenner. "A vocabulary for a multiscale process description for fast transmission and continuous visualization of spatial data." Computers & Geosciences 35.11 (2009): 2177-2184.
DOI: 10.34808/p8dv-a110
title: The generalization of objects representing groups of buildings in the Kartuzy district by simplification operator with the Simplify Building tool - scale 1:10000. Data from OSM.

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_10k_OSM _BuildingAggregation). The employed Simplify Building tool is described by the manufacturer Esri Inc. in the documentation [2]. As a result of the simplification process, several building groups have been removed.
Literature
[1] http://download.geofabrik.de/ [access: 26.04.2021].
[2] www.pro.arcgis.com [access: 15.11.2020]
DOI: 10.34808/5fwb-nf49
title: The aggregation of objects representing buildings in the Kartuzy district - scale 1:25000

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
The data cover the spatial range of Kartuzy district. In the district there are buildings of almost every class according to the BDOT10k classification except BUBD20. Single-family residential buildings account for 61% of all buildings, farm buildings for 31%, each of the other classes contains less than 2%.
The provided data include original data downloaded from geoportal.gov.pl (on 6 November 2020) [2] as well as processed data. The aggregation process was performed taking into account the proximity of the neighborhood [3] based on the minimum size of the figure [4, 5]. The data are the basis for the subsequent steps in the process of sequential generalization of building groups.
Literature
[1] Regulation of the Ministry of Administration and Digitization, W sprawie bazy danych obiektów topograficznych oraz bazy danych obiektów ogólnogeograficznych,
a także standardowych opracowań kartograficznych [Regarding the database of topographic objects and the database of general geographic objects, as well as standard cartographic studies.]
[2] www.geoportal.gov.pl [access: 06.11.2020].
[3] Kozioł, Krystian, and Michał Lupa. "The use of merging and aggregation operators for MRDB data feeding." Geoinformatica Polonica 12 (2013): 17-24.
[4] Chrobak, Tadeusz. Badanie przydatności trójkąta elementarnego w komputerowej generalizacji kartograficznej. Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, 1999.
[5] Chrobak, Tadeusz, et al. "A method for assessing generalized data accuracy with linear object resolution verification." Geocarto International 32.3 (2017): 238-256.
DOI: 10.34808/v3jf-2884
title: The generalization by simplification operator with Chrobak’s method of objects representing groups of buildings in Kartuzy district  - scale 1:25000

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_BuildingAggregation). The Chrobak's operator was designed to simplify lines [2]. For the purpose of simplifying buildings, this method has been adapted and extended by verifying the minimum size of the object using the cartographic matrix [3].
The resulting objects require another stage - orthogonalization. As a result of the simplification process, 1.5% of building groups have been removed.
Literature
[1] Regulation of the Ministry of Administration and Digitization, W sprawie bazy danych obiektów topograficznych oraz bazy danych obiektów ogólnogeograficznych,
a także standardowych opracowań kartograficznych [Regarding the database of topographic objects and the database of general geographic objects, as well as standard cartographic studies.]
[2] Chrobak, T. "Metoda uogólnienia danych w procesie generalizacji obiektów liniowych." Archiwum Fotogrametrii, Kartografii i Teledetekcji 13 (2003).
[3] Chrobak, Tadeusz. "Przydatność osnowy kartograficznej i metody obiektywnego upraszczania obiektów do aktualizacji danych w BDT." Geomatics and Environmental Engineering 3 (2009): 81-90.