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A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)
<p>Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837</p>
PERCEIVE: WP4: Spatial determinants of policy performance and synergies: Task4.4: Cohesion Policy vs Urban and Rural policies to address spatial discrepancies in EU territorial policy
<p>This dataset consists of data addressing the relationship between territorial cohesion objectives and the problems perceived by citizens. In particular a comparative analysis between the case study regions will generate data useful for identifying best practices in mixing the EU policy instruments for a better achievement of regional needs. Data that will be generated via focus groups interviews among representatives of LMA (local management authorities) in 2 Polish regions: Dolnośląskie and Warmińsko-Mazurskie . Interview transcripts and report was used to address how territorial cohesion objectives match the “real problems” of regions. The focus groups were built around the following main topics: 1) governance of the Cohesion Policy projects, in order to understand how different authorities at different levels cooperate and share the responsibilities for the implementation of the Cohesion Policy; 2) level of citizen engagement, in order to understand whether a bottom-up approach is used; 3) how the media inform on the Cohesion Policy programmes, in order to appreciate the discrepancies (if any) about the aims of Cohesion Policy and its construction on the public discourse. Comparing current and past programming periods, we investigate how the policy performs in reducing the gap between territorial cohesion objectives and “real problems” defined by LMAs and citizens. Because it is project-specific data and reflects the concept and methodology of the study under PERCEIVE they are perceived as unique - similar data does not exist. Potential users are be Regional Policy’s European/National/Local policy makers and practitioners, European networks and associations looking to data on LMA opinions on cohesion policy implementation in Poland to be used in policy recommendation, studies and policy making process; next group of potential data users are researchers working on assessment of Cohesion Policy, data may be used as a source for topic-related studies, case studies, comparisons.</p> <p>The dataset is made up of 5 files: 2 files consist of reports from the workshops, 2 files consist in transcripts of interviews to practitioners, beneficiaries and targets of the Cohesion Policy projects in the Polish selected case‐study regions. Interviewees are asked to provide their views and perceptions on the multilevel governance system, on the communication activities of the Operational Programmes and on the effectiveness of Cohesion Policy. 1 readme file is included.</p>
Urban-rural life tables for Scotland, 1861-1910
<p>This data set contains the life tables the were computed for the study presented in: Torres, C., V. Canudas-Romo, and J. Oeppen (2019) 'The contribution of urbanization to changes in life expectancy in Scotland, 1861–1910', <em>Population Studies</em>, 73:3, 387-404, DOI: 10.1080/00324728.2018.1549746</p> <p>The life tables are by sex and urban-rural category. For reasons explained in the paper, the tables cover periods of different lengths, from 1861 to 1910.</p> <p><strong>Example of how to load the data in R:</strong></p> <p>LT <- read.table("Urban-Rural-LifeTables-Scotland-1861-1910.txt", header = T, sep = ";")</p> <p><strong>Description of each column:</strong><br> Period: time-interval, including the first and excluding the last indicated years (e.g., [1861,1866) corresponds to the years from 1861 to 1865). Available periods: 1861-1865, 1866-1870, 1871-1874, 1875-1877, 1878-1880, 1881-1885, 1886-1890, 1891-1892, 1893-1896, 1897-1900, 1901-1905, 1906-1910.<br> Population: Rural, Semi-Urban, Urban, or Total population (see definitions in Torres et al. 2019)<br> Sex: Female or Male<br> x : Age (from 0 to 110+, by single ages)<br> nmx: Death rate in the age interval [x, x+n)<br> nax: average number of person-years lived in the age interval [x, x+n) by those who die in that interval<br> nqx: Probability of dying in the age interval [x, x+n)<br> lx: number of survivors at exact age x, or probability of surviving until exact age x<br> ndx: Life-table deaths in the age interval [x, x+n)<br> nLx: Person-years lived in the age interval [x, x+n)<br> Tx: Person-years lived above age x<br> ex: Remaining life expectancy at age x</p> <p>For more information about life tables in general, see: Preston, S., Heuveline, P., and Guillot, M. (2001). <em>Demography: Measuring and Modeling Population Processes</em>. Wiley-Blackwell</p>
Figure 3 in Ecological aspects and molecular detection of Leishmania DNA (Kinetoplastida: Trypanosomatidae) in phlebotomine sand flies (Diptera: Psychodidae) from a rural settlement in the Eastern Amazon, Brazil
Figure 3 Abundance of Phlebotominae Sand flies from Perimetral Norte Rural Settlement, Pedra Branca Municipality, Amapá State, Brazil, collected from February 2018 to February 2019, at the collection sites: ID intradomicile, PD peridomicile, F100m forest 100m from edge, F400m forest from edge. The letters "a" and "b" represent the significant difference.
Figure 2 in Ecological aspects and molecular detection of Leishmania DNA (Kinetoplastida: Trypanosomatidae) in phlebotomine sand flies (Diptera: Psychodidae) from a rural settlement in the Eastern Amazon, Brazil
Figure 2 Shannon's Index of Phlebotominae Sand Flies from Perimetral Norte Rural Settlement, Pedra Branca Municipality, Amapá State, Brazil, collected from February 2018 to February 2019, at the collection- sites: ID intradomicile, PD peridomicile, F100m forest 100m from edge, F400m forest from edge. The letters "a" and "b" represent the significant difference.
UrbanOccupationsOETR_temettuat_cultivation_CLC_6_region_rural_geosample
<p>With the UrbanOccupationsOETR, a European Research Councill, Starting Grant funded (Grant Number 679097, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, <a href="https://urbanoccupations.ku.edu.tr/">UrbanOccupationsOETR</a>) project hosted at Koç University 2016-2022, we wanted to highlight the importance of rural economic dynamics to explain differences in long-term regional economic development in the late Ottoman Empire. We provide an Excel dataset on the crop-specific agricultural mix and land area of an Ottoman region, Bursa, in the 1840s. This dataset is the result of a new geosampling methodology we devised, representing a novel development in the agricultural and overall economic history of Southeast Europe and the Middle East.</p> <p>The 1840s serve as a good period to choose for base years mainly due to three main factors to sample economic data on a regional scale. First, due to <em>Tanzimat</em> reforms (planned and only partially accomplished transformation of the Ottoman central administration in the mid-nineteenth century), the 1840s marked a watershed of bureaucratical information gathering. Especially, the <em>temettuat</em> registers were created as a by-product to realize a drastic change in tax collection. With at least in its first iteration, the unsuccessful abolishment of tax-farming by the Tanzimat decree in 1839, the Ottoman central administration aimed to transform the existing indirect and communal taxation with direct and individual modalities. To accomplish this goal, the administration had to survey the tax base, which was in disguise due to centuries-long tax farming practices. The <em>temettuat </em>registers were conducted in the core regions of the empire with the main exception of the imperial capital, Istanbul. Second, the 1840s correspond to the last period before the beginning of drastic territorial losses, primarily in Southeast Europe, which triggered in size and frequency unprecedented waves of emigration and immigration between the core territories of the empire both in Southeast Europe as well as in Anatolia, which continued until the official demise or the implosion of the empire. Third and lastly, the 1840s serves as a very suitable point to assess the dynamics of pre-industrial and <em>ancienne</em> regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities.</p> <p>The temettuat surveys are invaluable resources for they provide agricultural asset- / crop-type specific agricultural mix information with cultivation area per household. However, extracting their detailed information requires a team and years. To overcome this, we developed a sampling strategy that selected five locations per subdistrict using the Analytical Hierarchy Process (AHP), considering factors of agricultural suitability (85% weight), connectivity to historical roads (within a 500-meter to the closest road or to the Danube, 15% weight, justified by its impact on suitability), and subdistrict population size (chosen villages must represent at least 5% of the subdistrict's total population).</p> <p>Our geosampling methodology of the 1840s tax registers (<em>temettuat</em>) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (<em>sancak</em>) and subdistrict (<em>kaza</em>)) level total area of cultivation and shares of the agricultural mix for key products. We are using two mid-nineteenth-century datasets: Ottoman tax (TMT) (<em>temettuat</em>) surveys for crop type and cultivation area and the population (<em>nüfus</em>) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, <em>Deutsche Heereskarte </em>(DHK). The agricultural suitability raster is an amalgamation of the Land Capability Classification (LCC) encapsulating the variables of soil quality and quantity and the Digital Elevation Model (DEM) based on Shuttle Radar Topography Mission with 30-meter-resolution and comprising elevation, slope, and ruggedness data.</p> <p>In the end, a geosampling initiative was undertaken across six regions in Southeast Europe and Anatolia, namely Ankara, Bursa, Plovdiv, Ruse, Manisa, and Edirne, covering a total of 277 locations with 17,675 households. Our project team entered the economic data from those records into a Microsoft Access database. We employed a specially crafted data entry template to organize the tax survey data into multiple categories systematically.</p> <p>After geosampling locations, our objective extended to deriving estimates for the total cultivated area within each subdistrict and region. To achieve this goal, it was imperative that the data undergoes coding the cultivation areas into a standardized and comparable land-use scheme. We adopted the Corine Land Cover (CLC) nomenclature from the European Union's Earth Observation Programme (Copernicus), established in 1985 and regularly updated. Our study followed the revised guidelines issued by the European Environment Agency on 10.05.2019. Despite its primary design for contemporary land cover analysis, CLC nomenclature proved well-suited for accurately representing the agricultural tax data and the historical context of the 1845 Ottoman tax surveys.</p> <p>In our analysis, we coded micro-level cultivated land entries associated with individual households by using CLC's highest detail level. Successfully, every cultivated land entry was coded into the third level of detail in CLC, encompassing sub-categories such as 2.1 – “Arable land”, 2.2 – “Permanent crops”, 2.3 – “Pastures”, and 2.4 – “Heterogeneous agricultural areas”—all falling under the overarching category of 2 - Agricultural areas. Additionally, we coded entries related to 3.1 - “Forest” and 3.2 – “Shrub and/or herbaceous vegetation associations”, falling under the primary category of 3 – “Forest and seminatural areas.”</p> <p>Finally, cultivation areas expressed in Ottoman measurement units like <em>dönüm</em> (1/9,2 of a hectare) were converted into hectares to ensure consistency and ease of spatiotemporal comparison.</p> <p>This Zenodo dataset offers agricultural data for the entire geosampling area per household. It includes 39,002 agricultural entries coded according to CLC, detailing both the quantity and the cultivated area, corresponding to 14,997 individuals across 13,564 households. Please note that this is a rural geosample. Although the tax surveys of the primary (urban) and secondary (subdistrict centers) locations of all regions were read and entered, they are not included in this dataset.</p> <p>The categories and descriptions of the variables of the geosample dataset are as follows:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p> </p> <p>“GeoCode”</p> </td> <td> <p> </p> <p>UniqueID belonging to a specific geosampled location</p> </td> </tr> <tr> <td> <p> “Longitude” & “Latitude”</p> </td> <td> <p>Geographical coordinates used to specify the precise location of a geosampled location on the Earth's surface</p> </td> </tr> <tr> <td> <p>“Region” & “SubDistrict” & “Location”</p> </td> <td> <p>Geographic unit of entry, including region (district/<em>sancak</em>); subdistrict (<em>kaza</em>); and geosampled location as they appear in the population registers</p> </td> </tr> <tr> <td> <p>“RegisterNo”</p> </td> <td> <p>Archival code of the population register whose data is being entered</p> </td> </tr> <tr> <td> <p>“HaneNo”</p> </td> <td> <p>Number of the household (specified by the registers as <em>Hane</em>), as appears in the register</p> </td> </tr> <tr> <td> <p>"HouseID"</p> </td> <td> <p>Unique ID belonging to a specific household, automatically generated by Microsoft Access</p> </td> </tr> <tr> <td> <p>"IndividualID"</p> </td> <td> <p>Unique ID belonging to a specific individual, automatically generated by Microsoft Access</p> </td> </tr> <tr> <td> <p>"AgrID"</p> </td> <td> <p>UniqueID belonging to a specific agricultural asset / crop belonging to an individual</p> </td> </tr> <tr> <td> <p>"Cultivation"</p> </td> <td> <p>Type of the agricultural asset / crop</p> </td> </tr> <tr> <td> <p>"CLC_Cultivation_Code"</p> </td> <td> <p>CLC-code of the agricultural asset / crop</p> </td> </tr> <tr> <td> <p>"CtgUnit"</p> </td> <td> <p>Is applicable when the quantity of an agricultural asset or crop is specified using specific terms ("aded", "res", "eşcar", "sak" [usually for individual trees]), and when the area of an agricultural asset or crop is described in vague terms ("bab", "kıta" [usually for fields, gardens, and vineyards])</p> </td> </tr> <tr> <td> <p>"Unit"</p> </td> <td> <p>Quantity of the "CategoryUnit"</p> </td> </tr> <tr> <td> <p>"CtgArea"</p> </td> <td> <p>The land area type of the agricultural asset / crop in Ottoman measurement units, "Dönüm" and “Evlek” (1/4 of a “Dönüm”)</p> </td> </tr> <tr> <td> <p>"Area"</p> </td> <td> <p>Quantity of the "CategoryArea"</p> </td> </tr> <tr> <td> <p>“Area_Ottoman_dönüm”</p> </td> <td> <p>"Area" converted into Ottoman “Dönüm”</p> </td> </tr> <tr> <td> <p>“Area_hectare”</p> </td> <td> <p>"Area" converted into hectares</p> </td> </tr> </tbody> </table> <p> </p>
Fig. 6 in Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)
Fig. 6. Ordenamiento por medio de un PCA de las muestras, mostrando diferencias en la fauna de tardigrados por habitat en la porciÓn central de la provincia de Salta, Argentina.
Fig. 1 in Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)
Fig. 1. LocalizaciÓn de los sitios de muestreo en la porciÓn central de la provincia de Salta, Argentina.
Fig. 5 in Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)
Fig. 5. NMS donde se observa el ordenamiento de las muestras, mostrando diferencias en la fauna de tardÍgrados por habitat en la porciÓn central de la provincia de Salta, Argentina (Eje1=0.677, Eje2=0.054), con un Stress=0.077.
Fig. 4 in Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)
Fig. 4. DistribuciÓn de las abundancias relativas por habitat de las especies de tardÍgrados en la porciÓn central de la provincia de Salta, Argentina.
Fig. 3 in Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)
Fig. 3. ComparaciÓn de la estructura de las comunidades Urbana y Rural de tardÍgrados en la porciÓn central de la provincia de Salta, Argentina, mostrando diferencias entre la composición de las especies dominantes y raras.
Fig. 2 in Molecular detection of Cryptosporidium parvum in wild rodents (Phyllotis darwini) inhabiting protected and rural transitional areas in north-central Chile
Fig. 2. Phylogram representing analysis of the 18 rRNA region. The evolutionary history was inferred with maximum likelihood method and the Tamura 3-parameter (T92) model with a discrete Gamma distribution (5 categories (+G)). Analysis contains sequences uploaded from GenBank (with Cryptosporidium species, host, country, and accessions numbers in brackets) and those obtained in the present study are shown in triangles (with ID isolate, host, site of sampling and country, and accessions numbers in brackets). Bootstrap values are represented as per cent of internal branches (1000 replicates), and values lower than 50 are hidden. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Cryptosporidium muris was used to root the tree.
Fig. 1 in Molecular detection of Cryptosporidium parvum in wild rodents (Phyllotis darwini) inhabiting protected and rural transitional areas in north-central Chile
Fig. 1. Map of the Coquimbo region in Chile showing the two types of areas (i.e., Bosque Fray Jorge National Park - BFPNP [in green]; El Tangue Farm [in blue]) in which Darwin's leaf-eared mice (Phyllotis darwini) were sampled. In each area, 4 grids were established, and 200 capture points were allocated per grid. (UTM projection. Datum WGS84, Zone 19J).
Fig. 2 in A single Haemoproteus plataleae haplotype is widespread in white ibis (Eudocimus albus) from urban and rural sites in southern Florida
Fig. 2. Typical Haemoproteus plataleae stages from three infected white ibis (Eudocimus alba) from South Florida. All ibis were genetically confirmed to be infected with the EUDRUB01 lineage. A-E, an ibis from Juno Beach urban park; F, an ibis from Indian Creek urban park; and G-L, an ibis from the Solid Waste site. The latter bird had rare round forms (K-L), which were absent from other H. plataleae-infected ibis. Younger stages (C, H, I) had a an evident 'cleft' between the gametocyote and erythrocyte nucleus.
Fig. 3 in A single Haemoproteus plataleae haplotype is widespread in white ibis (Eudocimus albus) from urban and rural sites in southern Florida
Fig. 3. Phylogenetic relationship of Haemoproteus plataleae from white ibis (Eudocimus albus) with other Haemoproteus spp.
Fig. 1 in A single Haemoproteus plataleae haplotype is widespread in white ibis (Eudocimus albus) from urban and rural sites in southern Florida
Fig. 1. Box plots of parasitemia values of Haemoproteus plataleae in white ibis (Eudocimus albus) sampled from South Florida from 2010 to 2022 by year (A.), season (B.), and age (C. and D.). C. shows all ibis with general adult vs. juvenile age class designations and D. shows data for the subset of ibis that were aged to specific year for juveniles (1, 2, or 3 yrs old). Years 2015 and 2017 were significantly different from each other, but both were similar to other years. For remaining figures, factors that are differently colored are significantly different from each other. Note that the x-axis maximum varies between plots.
Fig. 1 in Surveillance and genotype characterization of zoonotic trypanosomatidae in Didelphis marsupialis in two endemic sites of rural Panama
Fig. 1. Map showing the communities of Las Pavas (LP) (top set of images) and Trinidad de Las Minas (TM) (bottom set of images) with the number of opossums captured and infected with T. cruzi in the 3 collection sites in each community. A. Map with the geographic location of the LP and TM communities in the country of Panama. Satellite view of the P: Peridomicile (B), R1: remnant 1 (C) and R2: remnant 2 (D) collection site each with its 4 transects in the LP community. Satellite view of the P: Peridomicile (E), R1: remnant 1 (F) and R2: remnant 2 (G) collection site each with its 4 transects in the TM community.
Figure S2 in Plant diversity and conservation value of wetlands along a rural-urban gradient
Figure S2. MDS ordination indicating the clear separation of the two land use groups based on the urbanisation measures.
Figure 6. A in Plant diversity and conservation value of wetlands along a rural-urban gradient
Figure 6. A, Percentage distribution of alien and indigenous species per site; B, the indigenous (ISR) and alien (ASR) species richness per site; C, the percentage of the total average cover of all alien species per site; D, the associated adjusted Floristic Quality Assessment Index values (adjFQAI) of each site; arranged along a gradient of increasing percentage urban landcover.
Figure S1 in Plant diversity and conservation value of wetlands along a rural-urban gradient
Figure S1. Cluster analysis results based on the urbanisation measures indicating clear grouping between the urban sites 1 and 2 and the rural sites.
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