Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Biocultural ecoutourism values per municipality in Colombia

A dataset with eight columns with the output values for the analyses done in the Echeverri, et al. (2024) paper published in People and Nature called: "Toward Sustainable Biocultural Ecotourism: An Integrated Spatial Analysis of Cultural and Biodiversity Richness in Colombia" This data set has the following components: [1]Code_DANE: Unique code ID assigned to each municipality according to the National Statistics Bureau DANE to each of the 1,121 municipalities [2]Departamento: Name of the Departamento (equivalent to state or province) for each municipality [3]Region: Ecoregion where the municipality is located (either in Amazonia, Orinoquia, Caribbean, Pacific, Andean) [4]Biodiversity_index_scaled: Results of the biodiversity richness index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value [5]Cultural_index_scaled: Results of the institutionalized cultural richness index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value [6]Accesibility_index_scaled: Results of the accesibility index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value [7]Demand_index_scaled: Results of the demand index calculated in this paper and scaled from 0 to 1, where 1 is the highest possible value, and 0 is the lowest value

opencc-by-4.0Sep 2024View details →

Mean Land Surface Temperature in the Municipality of São Paulo between 2017 and 2023

Mean Land Surface Temperature (LST) and mean Normalized Difference Vegetation Index (NDVI) for each pixel in the municipality of São Paulo, in Brazil, using Landsat 8 data from 2017-01-01 to 2023-01-01 (6 years) and the algorithm from Ermida et al. (2020). The average overpass time is 10:04 a.m. in the local time (GMT-3) and the spatial resolution is 30 meters. Variables: <ul> <li>mean_LST: mean Land Surface Temperature [Celsius (oC)]</li> <li>mean_NDVI: mean NDVI</li> <li>*_sd: Standard deviation of the mean</li> <li>RED, GREEN, BLUE and NIR: mean reflectance of each band</li> </ul> Acknowledgments: FAPESP grant 2021/11762-5

opencc-by-4.0Jul 2021View details →

Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines

This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines. In this version, occurrence.csv was revised as described below: <ul> <li>taxonID for Abudefduf vaigiensis (Quoy & Gaimard, 1825) and  Hemiaulus P.A.C. Heiberg, 1863  were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>

opencc-by-4.0Dec 2022View details →

EU FAR Database: EU Funds absorbed by Romanian Municipalities 2016-2021

EU funds reported by each locality (municipality) in their annual budgets execution reports. The data is processed by the authors, based on the information published by the Ministry of Development, Public Works and Administration, Directorate for Local Fiscal and Budgetary Policies.

opencc-by-4.0Jan 2023View details →

Processed cadastre map for Aarhus municipality 2018

Cadastre map for Aarhus municipality The whole of Denmark is divided into cadastres, which represent legally binding property boundaries. The 2018 cadastre map comprises a total of 81,437 cadastre units within Aarhus municipality. The cadastre map contains some general land use information, such as whether a cadastre unit is part of the road or rail network and if it is primarily used for agricultural production. Furthermore, each cadastre unit contains a unique identification string, which can be linked to building specific information from the Danish Building and Dwelling (BBR) register and to ownership information from the Central Business (CVR) register. Based on this information, for the 2018 cadastre, we compiled information on ownership categories, building specific information (e.g. use, such as residential, business etc.) and whether a cadastre unit is publicly accessible. For some of the information, the cadastre map was overlaid with other spatially explicit datasets. The different columns in the compiled cadastre map are described in the table below Information contained in the 2018 cadastre map for Aarhus municipality <table> <tbody> <tr> <td> Column Name </td> <td> Description </td> <td> Unit </td> <td> Comment </td> </tr> <tr> <td> parcel_id </td> <td> Unique ID for cadastre parcel </td> <td> Number </td> <td> </td> </tr> <tr> <td> cvr_name </td> <td> Owner name from CVR (central business) register </td> <td> Text </td> <td> </td> </tr> <tr> <td> road_rail_code </td> <td> Code depicting if a cadastre is designated as road or railway line: 0 = Other; 1 = road; 2 = rail </td> <td> Number </td> <td> Based on information contained in the cadastre dataset </td> </tr> <tr> <td> road_rail_name </td> <td> Name for cadastres designated as roads or railways </td> <td> Text </td> <td> </td> </tr> <tr> <td> code_BBR </td> <td> Code depicting use of buildings from BBR (building and dwelling register) </td> <td> Number </td> <td> In case of more than one building use, the cadastre is assigned the building use with the largest area within the cadastre </td> </tr> <tr> <td> name_BBR </td> <td> Name for use of buildings from BBR (building and dwelling register) </td> <td> Text </td> <td> </td> </tr> <tr> <td> built_year </td> <td> Building year of oldest existing building on the cadastre </td> <td> Year </td> <td> </td> </tr> <tr> <td> ownership_code </td> <td> Code for type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_name </td> <td> Name for type of ownership </td> <td> Text </td> <td> </td> </tr> <tr> <td> ownership_agg_code </td> <td> Code for aggregated type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_agg_name </td> <td> Name for aggregated type of ownership </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> public_access_code </td> <td> Code depicting if a cadastre is publicly accessible: 0 = No public access; 1 = public access; 2 = road </td> <td> Number </td> <td> cadastres depicted as publicly accessible include: all publicly owned areas, unless they are farmed; common areas owned by private owner/housing associations; common areas owned by cooperative housing associations; private forests; privately owned nature areas, if these are not fenced </td> </tr> <tr> <td> public_access_name </td> <td> Name for public accessibility </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_01 </td> <td> Code for building use for 1st aggregation level </td> <td> Number </td> <td> Based on aggregation of use categories from BBR (building and dwelling register) </td> </tr> <tr> <td> build_name_01 </td> <td> Name for building use for 1st aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_02 </td> <td> Code for building use for 2nd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_02 </td> <td> Name for building use for 2nd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_03 </td> <td> Code for building use for 3rd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_03 </td> <td> Name for building use for 3rd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> public_access_greenspace_type_code </td> <td> Code for type of publicly accessible green space: 0 = No publicly accessible green-blue space; 1 = Institution; 2 = Common area; 3 = Cemetery; 4 = Sport/leisure; 5 = Other green-blue space </td> <td> Number </td> <td> Based on combination of ownership, building type and public accessibility </td> </tr> <tr> <td> public_access_greenspace_type_name </td> <td> Name for type of publicly accessible green space </td> <td> Text </td> <td> </td> </tr> <tr> <td> golf_course_code </td> <td> Code depicting if a cadastre is used as golf course: 0 = ND; 1 = golf course </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> golf_course_name </td> <td> Name for golf course </td> <td> Text </td> <td> </td> </tr> <tr> <td> riding_school_horse_racing_track_code </td> <td> Code depicting if a cadastre is used as riding school or horse racing track: 0 = ND; 1 = riding school/horse racing trach </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> riding_school_horse_racing_track_name </td> <td> Name for riding school/horse racing track </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> primary_school_code </td> <td> Code depicting if the cadastre belongs to a primary school: 0 = no primary school; 1 = primary school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_code </td> <td> Code depicting school type: 0 = No primary school; 1 = public school; 2 = public special school; 3 = private school; 4 = private continuation school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_name </td> <td> Name of school type </td> <td> Text </td> <td> </td> </tr> <tr> <td> primary_school_name </td> <td> Name of school </td> <td> Text </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_count_pupils </td> <td> Number of pupils attending school (2022) </td> <td> Number </td> <td> Based on school webpages </td> </tr> </tbody> </table>

openother-openJan 2023View details →

Official-Websites-of-the-Portuguese-Municipalities: v1.0.0

This repository contains a list of all 308 Portuguese municipalities, with their respective district and website. The information presented here was based on the update of January 19, 2023, and comes from the website of the General Directorate of Local Authorities (DGAL), which is the central service of the direct administration of the State integrated into the Ministry of Territorial Cohesion of Portugal [1]. Data collection took place on January 26, 2023. We also carried out a manual review of all the website addresses on the list, where we identified and corrected some incorrect email addresses through a Google search of the corresponding municipality.

opencc-by-4.0Apr 2023View details →

Zonal Statistics of Weather Indicators for Brazilian Municipalities from the TerraClimate Project

This dataset contains 14 parquet-format files with monthly data. <table align="center"> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>aet.parquet</td> <td>Actual Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>def.parquet</td> <td>Climate Water Deficit</td> <td>mm</td> </tr> <tr> <td>pdsi.parquet</td> <td>Palmer Drought Severity Index (PDSI)</td> <td>unitless</td> </tr> <tr> <td>pet.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ppt.parquet</td> <td>Potential evapotranspiration</td> <td>mm</td> </tr> <tr> <td>q.parquet</td> <td>Runoff</td> <td>mm</td> </tr> <tr> <td>soil.parquet</td> <td>Soil Moisture</td> <td>mm</td> </tr> <tr> <td>srad.parquet</td> <td>Downward surface shortwave radiation</td> <td>W/m2</td> </tr> <tr> <td>swe.parquet</td> <td>Snow water equivalent</td> <td>mm</td> </tr> <tr> <td>tmax.parquet</td> <td>Maximun Temperature</td> <td>°C</td> </tr> <tr> <td>tmin.parquet</td> <td>Minimum Temperature</td> <td>°C</td> </tr> <tr> <td>vap.parquet</td> <td>Vapor pressure</td> <td>kPa</td> </tr> <tr> <td>vpd.parquet</td> <td>Vapor Pressure Deficit</td> <td>kpq</td> </tr> <tr> <td>ws.parquet</td> <td>Wind speed</td> <td>m/s</td> </tr> </tbody> </table>

opencc-by-4.0May 2023View details →

Geospatial, biophysical and socioeconomic data for the Athens municipality on a zipcode resolution

A collated dataset from various remote sensing, local and national sources.

opencc-by-4.0Oct 2023View details →

Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 1950-2022

Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, from 1950 to 2022.

opencc-by-4.0Dec 2019View details →

Demographic, economic, geospatial data for municipalities of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) in 2010-2016

The database contains demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016. The sources of data are the municipal-level statistics of Rosstat, Google Maps data and calculated indicators. The statistical data were arranged by the year, the data on municipalities for which there were administrative and territorial transformations for the period under study were excluded (in some cases, the data were provided in accordance with the administrative-territorial demarcation as of 2016). Municipalities' websites were used to fill the lack of population information in individual municipalities for some years. Calculated variables were made to estimate a number of indicators per capita, to introduce additional demographic indicators (e.g. migration inflow rate), to bring price economic indicators to base year prices (2010). For example, indicators of income of the local budget, volumes of investments in fixed assets (excluding budgetary funds), level of wages are modified to a comparable form (to 2010 prices). The distances on roads in different units of measurement from the geographical center of municipalities to the center of the capital of the region are calculated using the Google Maps database. Data mapping was performed using ArcGIS software. The data set consists of 1) Municipalities_CFD_Russia_2010_2016_ENG.xlsx - The database of demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016, 2) MUNICIPALITIES_CFD_RUSSIA_SHAPE.rar - The shape-files for maps construction, 3) Fig.1. Municipalities ENG.jpg - The map of studied administrative units and municipalities of the Central Federal District in Russia .

opencc-by-4.0Sep 2019View details →

Alberta Municipal Water Supply Overview

This dataset contains information on the water source for municipalities in Alberta, Canada.  The data was collected between September 2018 and September 2019.

opencc-by-4.0Nov 2023View details →

Age-adjusted Covid-19 mortality rates for Brazilian municipalities

This dataset present Covid-19 crude and age-adjusted mortality rates for Brazilian municipalities from 2020 to 2022 per epidemiological week, on 100,000 inhabitants base. The mortality data source is the "Sistema de Informações de Mortalidade -- SIM", available at https://opendatasus.saude.gov.br/dataset/sim .The population reference is the Brazilian age-structure at 2020.Notebook with method and code: https://rfsaldanha.github.io/posts/std_br_covid_rates.html

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-4.0Mar 2024View details →

Elva Municipality Diagnosis Dataset

This dataset contains the information used in the  drOp project (Digitally enabled social district renovation processes for age-friendly environments driving social innovation and local economic development. Num.101080025) to build a multidimensional characterization of Elva and Nooruse neighbourhood through relevant indicators, statistical data, as well as other urban and social data available.

Fig. 2 in Leguminosae no Parque Natural Municipal de Jacarenema, Vila Velha, Espírito Santo, Brasil

Fig. 2. Comparação entre a diversidade de Leguminosae nas restingas do Brasil. 1. Plano de Manejo do Parque Natural Municipal de Jacarenema, ES (Gomes 2002); 2. Reserva de Camburi, ES (Pereira & Assis 2000); 3. Morada do Sol, ES (Valadares et al. 2011); 4. Parque Estadual de Itaúnas, ES (Souza et al. 2016); 5. Parque Estadual Paulo César Vinha, ES (Chagas et al. 2014, Peterle et al. 2015, Weiler Jr. 1998); 6. Reserva Ecológica Estadual de Jacarepiá, RJ (Sá 1992); 7. Área de Proteção Ambiental da Massambaba, RJ (Araújo et al. 2009); 8. Bertioga, SP (Martins et al. 2008); 9. Reserva Particular do Patrimônio Natural de Maracaípe, PE (Almeida Jr et al. 2009); 10. Área de Proteção Ambiental de Guadalupe, PE (Cantarelli et al. 2012); 11. Parque Nacional Serra de Itabaiana, SE (Dantas et al. 2010); 12. Ceará (Santos-Filho et al. 2011); 13. Piauí (Santos-Filho 2009).

Figs. 3A-R. A. Alysicarpus vaginalis – Folha. B-D. Chamaecrista rotundifolia var. rotundifolia. B in Leguminosae no Parque Natural Municipal de Jacarenema, Vila Velha, Espírito Santo, Brasil

Figs. 3A-R. A. Alysicarpus vaginalis – Folha. B-D. Chamaecrista rotundifolia var. rotundifolia. B. Folíolo; C. Flor; D. Fruto. E-G. Zornia glabra. E. Folíolos; F. Inflorescência; G. Fruto. H, I. Stylosanthes gracilis. H. Estípulas; I. Folha; J. Stylosanthes scabra - Folha. K. Crotalaria maypurensis - Estípula. L, M. Crotalaria pallida. L. Fruto; M. Estípula. N. Ancistrotropis peduncularis - Fruto. O, P. Desmodium adscendens; O. Fruto; P. Flor. Q. Rhynchosia phaseoloides - Fruto. R. Centrosema virginianum Flor.

Figs. 5A-O. A in Leguminosae no Parque Natural Municipal de Jacarenema, Vila Velha, Espírito Santo, Brasil

Figs. 5A-O. A. Dalbergia ecastaphyllum - Inflorescência; B-D. Zollernia glabra. B. Fruto; C. Inflorescência; D. Fruto. E. Hymenaea fariana - Folha. F, I. Swartzia simplex. F. Folha; I. Fruto. G, H. Sophora tomentosa. G. Folha; H. Fruto. J. Machaerium lanceolatum - Fruto. K. Leptolobium tenuifolium - Fruto. L. Andira nitida - Fruto. M. Swartzia apetala - Fruto. N. Machaerium hirtum - Ramo armado. O. Mimosa pudica - Ramo aculeado.

Figs. 4A-Q. A. Canavalia parviflora - Fruto. B, C. Aeschynomene brasiliana. B in Leguminosae no Parque Natural Municipal de Jacarenema, Vila Velha, Espírito Santo, Brasil

Figs. 4A-Q. A. Canavalia parviflora - Fruto. B, C. Aeschynomene brasiliana. B. Estípula; C. Fruto. D-F. Chamaecrista ramosa. D. Estípula; E. Flor; F. Fruto. G. Chamaecrista nictitans subsp. patellaria - Nectário foliar. H. Chamaecrista nictitans subsp. disadena - Nectário foliar. I. Senna appendiculata - Nectário foliar. J. Chamaecrista ensiformis - Nectário foliar. K. Inga edulis - Nectário foliar. L. Chamaecrista ensiformis - Flor. M. Inga edulis - Flor. N. Zygia latifolia var. glabrata - Fruto. O. Inga edulis - Fruto. P. Inga laurina- Inflorescência. Q. Inga subnuda - Inflorescência.

opencc-by-4.0Dec 2011View details →

Fig. 1 in Materials About The Fauna Of Beetles (Insecta: Coleoptera) Of Naujene Rural Municipality (Daugavpils District, Latvia). Part 3

Fig. 1. Location of research area. Naujene rural municipality: 1 – Vecstropi, 2 – Krauja, 3 – Stropi, 4 – 0,5 km E from Križi (Daugavpis City), 5 – Kašatniki, 6 – Lociki, 7 – Naujene, 8 – Juzefova park, 9 – Vecpils, 10 – Butiški, 11 – Vasargeliški, 12 – Bukšti, 13 – Slutiški.