Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

MeerTime MSP Census

Data table and PSRFITS-format files associated with Spiewak et al. 2022, "The MeerTime Pulsar Timing Array -- A Census of Emission Properties and Timing Potential" (doi:10.1017/pasa.2022.19). The zipped tar file contains one PSRFITS-format file per pulsar, containing a profile, summed from multiple observations with varying lengths, with 1024 phase bins across the profile, 1 subintegration, 8 frequency channels across the 775.75-MHz band, and 4 polarization channels (with the up-to-date rotation measures and dispersion measures in the headers).  The columns in the data table (in csv format) are as follows:  PSRJ : the pulsar name (J2000) PTA_SCR : boolean indicating whether the pulsar is part of the MPTA REF_SH : short form of the reference (psrcat-style) GL, GB : Galactic longitude and latitude P0, P1 : pulse period and period derivative in (s) and (s/s), respectively DM, DM_ERR, DMEPOCH : the dispersion measure of a single epoch, with 1-sigma uncertainty, and the MJD for that epoch RM, RM_ERR : the rotation measure, as described in the paper, with uncertainty RM_SNG : boolean indicating whether the RM was measured from the summed archive (True indicates the RM_ERR is the 1-sigma uncertainty from `rmfit` and False indicates the RM_ERR is the standard deviation of all measured RMs after outlier removal) L_FRAC, L_FRAC_ERR : the fractional linear polarization and uncertainty V_FRAC, V_FRAC_ERR : the fractional circular polarization and uncertainty ABS_V_FRAC, ABS_V_FRAC_ERR : the fractional absolute circular polarization (from `psrchive`) and uncertainty TOA_ERR_NEW : the median ToA uncertainty, as described in the paper SPIND, SPIND_ERR : the spectral index from a power law fit to the flux density per band and uncertainty NOBS : number of unique observations of the pulsar included in this analysis S1400, S1400_ERR : the flux density at 1400 MHz from a power law fit to the flux density per band, and uncertainty FLUX_n_VAL, FLUX_n_ERR, FLUX_n_FRQ : flux density information per frequency band (n=0-7), the mean value, the formal error on the mean, and the mean frequency of the band html, body, table, thead, input, textarea, select {color: #bab5ab!important; background: #35393b;} input[type="text"], textarea, select {color: #bab5ab!important; background: #35393b;} [data-darksite-inline-background-image-gradient] {background: linear-gradient(rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 0.5))!important; -webkit-background-size: cover!important; -moz-background-size: cover!important; -o-background-size: cover!important; background-size: cover!important;} [data-darksite-force-inline-background] * {background-color: rgba(0,0,0,0.7)!important;} [data-darksite-inline-background] {background-color: rgba(0,0,0,0.7)!important;} [data-darksite-inline-color] {color: #fff!important;} [data-darksite-inline-background-image] {background-image: linear-gradient(rgba(0,0,0,0.3), rgba(0,0,0,0.3))!important}

opencc-by-4.0Jul 2022View details →

District Reconstruction from Peruvian Censuses, 1876-2017

District Reconstruction from Peruvian Censuses, 1876-2017

opencc-by-4.0Jul 2022View details →

Population Census Peru 1876-2017

Population Census Peru 1876-2017

opencc-by-4.0Sep 2022View details →

Database of Rural Technological Trajectories, their variants and territories featured by peasantries of the Brazilian Northern Region based on Agricultural Censuses and special tabulations for the Economy of Agroforestry Systems (2006 and 2017)

Introduction This database contains the string variables at municipal level that qualifies the techno-productive trajectories (TT) of the Brazilian Northern Region Agrarian Economy, their technological variants (TTV) and territories based on peasantries. The TTs and TTVs were defined and theoretically justified by Costa (2021, p. 217-219). Delimitation of Technological Trajectories, their variants and territories featured by peasantries The TTs are designed by a method that combines differentiation and structural signification of rural production in each territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU). Structural differentiation (Phase 1) is necessary because production systems activities play different roles, depending on the systems  production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 440 and 441 of Costa (2021). In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs structures that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The codes of variable “Technological Trajectories” in this database are “CamponêsT1” for “Peasant Trajectory.T1” in Costa, 2021; “CamponêsT2” for “Peasant Trajectory.T2”; “CamponêsT3” for “Peasant Trajectory.T3”; “PatronalT4” for “Employer.T4”; “PatronalT5” for “Employer.T5”; “PatronalT7” for “Employer.T7”. In turn, the procedures to get the technological variants of TTs (TTVs) for census years 2006 and 2017 are described in Costa, 2021, p. 447-451. The codes of variable “Technological Variants” in this database are “IQ” for “CI = Chemical Intensity” in Costa, 2021; “IM” for “MI = Mechanical Intensity”; “IT” for “LI = Labour Intensity”; “IPst” for “PI = Pasture Improvement”; “IReb” for “HI = Herd improvement”; “Crg” for “LoadC=Load Capacity of Pasture”; “SAF-F” for “AFSs-F = AFSs with the presence of forest management”; “SAF-A” for “AFSs-A = Artificially developed AFSs”; “+” after the attribute for “Attribute clearly verified; “–“ for “Attribute clearly absent”; “0” for “an uncertain attribute”. The Brazilian Northern Region encompasses the municipalities of the federative states Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins. A municipality is codified by the variable “Peasantry” as “ACaboclo_Originário”, meaning a territory of an “original caboclo peasantry (OcP in English or CbO in Portuguese)”, if founded before 1880; “BCaboclo_Forâneo”, meaning a territory of an ”immigrant caboclo peasants (IcP or CbF)”, if founded between 1880 and 1910; “CAgrícola_Forâneo, meaning a territory of a “post-ruber immigrant agricultural peasantry (IpR or FpB)”, if founded  between 1910 and 1960; and “DContemporâneo”, meaning a “recent peasantry (ReP or ReC)”, if founded since 1960. The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 2006 and 2017 Agricultural Censuses. The following special cases were handled: <ul> <li>In the Agricultural Census 2017 credit data were not available. However, the Central Bank of Brazil informs for that year total rural credit for family-based and non-family-based agriculture and livestock by municipality.</li> <li>Comparing the production of manioc in the 2006 census with the production of manioc flour in the same year and with the historical production, we arrived at errors in three municipalities in Pará: in Moju a manioc production of 498,907 t is recorded adding the two sets of data (Peasant and Employer), when in fact it is 42,132; in São Miguel do Guamá the figure of 392,784 t is recorded when it actually is 175,941; 204,216 is recorded in Viseu and 106,287 is actual figure. Corrections were made using the proportion manioc/manioc flour prevailing in other municipalities in same microregion.</li> <li>In the census there are the value of the production of manioc and the value of the production of manioc flour. Since we are dealing with the same producer, if we consider in gross value of production or income aggregations both products, we incur double counting. In such cases, the value related to manioc flour was considered.</li> <li>The 2006 census has information on fishing restricted to the monetary income from the sale of fish, as a complementary income variable. The information does not incorporate the value of fish consumed in the establishment. Therefore, it is not a variable equivalent to the GVP of all other products considered. In turn, the 2017 census provides data on fish production as part of livestock (the gross value of fish production in captivity, which makes up the gross value of livestock production) but does not maintain the fish sales variable from the previous census. This income is contained in the variable “other producer income”, in which, none of the other possibilities listed (esgargot, etc.) are adhered to T2 (IBGE, Censo Agropecuário de 2017. Rio de Janeiro, IBGE, 2018). Therefore, two things were done to incorporate fisheries: a) for 2006 the GVP of fishery production was considered the variable "fish sale" under the heading "other producer income" divided by 1 minus the self-consumption rate 31% (Costa et al, 2022); b) for 2017, the GVP of fisheries production resulted from the division of the variable “other producer's incomes” by the same denominator of the operation described in “a”.</li> </ul> The dataset is organized as: 1. Data set with variables delimiting TT, TTV and Peasantry 2006_NorthRegion_TechVariants.csv 2017_ NorthRegion_TechVariants.csv. In each table the column names are self-explanatory. 2. Dataset with special tabulation or the agroforestry systems economy represented by Peasant Trajectory.T2     Gross Value of Production     Table1_NorthRegion_T2_GVP.csv      Table 1a – Gross Value of Productios (GVP) by products of AFSs-F and peasantry 2006 and 2017     Table 1b – Gross Value of Productios (GVP) by products of AFSs-A and peasantry, 2006 and 201     Table 1c – Gross Value of Productios (GVP) by products of T2, technological variant, and peasantry, 2006 and 2017       Real Product      Real Product” (RP): For each year (i), the vector of produced quantities (Qi) multiplied by a vector of fixed prices (P1): variation of RP is explained exclusively by the variation of Q.        Table2_NorthRegion_T2_RealProduct.csv       Table 2a – T2 Production by technological variant and peasantry, 2006       Table 2b – T2 Production Value (=Real Product) by technological variant, and peasantry, 2006 in R$ 1,000 currents       Table 2c – T2 Implicit prices by technological variant, and peasantry, 2006, R$ 1.000 currents (each cel in Table 2b divided by corresponding cel in Table 2a)       Table 2d – T2 Production by technological variant, and peasantry, 2017       Table 2e – T2 Real Product1 by technological variant, and peasantry, 2017 in R$ 1,000 from 2006 (each cel in Table 2c multiplied by corresponding cel in Table 2d)        Key variables       Table3_NorthRegion_T2_KeyVariables.csv        Table 3 – Key variables of T2 economy by peasantry, 2006 and 2017 Reference: Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2). COSTA, F. A., FEIJÃO,, L. G., ALMEIDA, I. C., NOGUEIRA, K. N. S., AMERICO, M. C. (2022). Database of a Riverine Economy in Mocajuba, Low Tocantins, Pará, Amazonia, Brazil [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121336

opencc-by-4.0Jun 2024View details →

Census-based mobility graph data across 12 U.S. metro regions

This dataset contains mobility information among census tracts for 12 U.S. cities in 2021. Mobility is defined to be the daily commute flow of residents, the data for which is retrieved from the Longitudinal Employer-Household Dynamics (LEHD), a U.S. Census Bureau program.  In each of the individual city's directory, the file 'city_network_edges.csv', the 'S000' column the commute flow number between 'origin' and 'destination' census tracts of the city. The geographical boundaries of census tracts are also present in each directory, with 'city_network_nodes' representing shapefiles.

Census of Marine Life: Handbook of Deep-Sea Hydrothermal Vent Fauna

Desbruyeres, Segonzak & Bright, eds. 2006. HANDBOOK OF DEEP-SEA HYDROTHERMAL VENT FAUNA. Denisia 18:1-455 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.376.2677&rep=rep1&type=pdf . ChEss (Chemosynthitic Ecosystem Science) is a field project of the Census of Marine Life programme (CoML). The main aim of ChEss is to determine the biogeography of deep-water chemosynthetic ecosystems at a global scale and to understand the processes driving these ecosystems. ChEss addresses the main questions of CoML on diversity, abundance and distribution of marine species, focusing on deep-water reducing environments such as hydrothermal vents, cold seeps, whale falls, sunken wood and areas of low oxygen that intersect with continental margins and seamounts.

Census of Marine Life: Pacific Ocean Shelf Tracking

The Pacific Ocean Shelf Tracking (POST) Project furthers understanding of the behaviour of marine animals through the operation of a large-scale ocean telemetry and data management system. POST serves as an accessible research tool for academe, resource agencies and the public. Long-term monitoring of marine animals contributes to the conservation and stewardship of marine resources.

Census of Marine Life: Census of Antarctic Marine Life

The Census of Antarctic Marine Life (CAML) is the project of the Census of Marine Life (CoML) devoted to Antarctica and investigates the distribution and abundance of Antarctic marine organisms, integrating knowledge across all regions, biomes, habitats and fields of study, with the final aim to make a census of all Antarctic species, from bacteria to megafauna. CAML__s main biodiversity data have been collected during the International Polar Year (IPY) (2007-2008) from 18 vessel-based research expeditions. This bulk of information is completed by ongoing surveys of unworked Museum and research collections which will complete the information available for each Antarctic species known so far. CAML__s biodiversity data are disseminated through CAML__s sister program SCAR Marine Biodiversity Information Network (SCAR-MarBIN) fully interoperable with the Ocean Biodiversity Information System (OBIS). CAML is also contributing to the Barcode of Life project (MarBOL) and iBOL, employing modern genomic techniques and coordinating the effort of different research groups to the final aim to have a barcode sequence for each Antarctic species of marine organisms. http://www.antarctica.gov.au/news/2009/australian-polar-research-at-close-of-ipy/census-of-antarctic-marine-life

Census of Marine Life: Census of Marine Zooplankton

The Census of Marine Zooplankton includes scientists, students and others interested in zooplankton from around the world who are working toward a taxonomically comprehensive assessment of biodiversity of animal plankton throughout the world ocean, a field project of the Census of Marine Life (see www.CoML.org). We are studying the 7,000 described species in 15 phyla that comprise the holozooplankton – animals that drift with ocean currents throughout their lives. We are determining a DNA barcode (short DNA sequence for species identification) for each species. We sponsor activities such as taxonomic training workshops, sponsors student and researcher visits to CMarZ laboratories. We maintain a distributed data system of biological and physical information from CMarZ related cruises. We provide image galleries of living plankton. http://www.cmarz.org/

opencc-by-4.0Sep 2024View details →

Egypt villages & urban neighbourhoods shapefile - 1996 census

This geographical dataset consists of a series of four shapefiles. The main one, EGY_SEC, provides the delineation for the villages (Yahya), cities (Madina), and neighbourhoods (Shiyakha) of Egypt's larger towns. This shapefile comprises 5410 geocoded geographical units linked with the 1996 census data. The EGY_SEC geographical layer of Egypt's smallest administrative units has been sourced from various paper map series (notably Egyptian administrative military maps and the 1/25.000 series). The layer is georeferenced using Egypt's datum (Egypt 1907 / Blue Belt). The previous work of Sylvie Fanchette1 on the Nile Delta population mapping has been instrumental in the making of this geodataset. It was created in the frame of the programme EGIPTE “Explorations Géographiques Informatisées de la Population et du Territoire de l’Égypte » funded by CNRS from 2004 to 2006 (PIR-Ville). It was coordinated by François Moriconi and Eric Denis with the support of Hala Bayoumi and Ahmed Wagih, and hosted during the 1990s’ by the CEDEJ in Cairo. An updated version of this map is used and can be viewed on the join website: <a href="https://www.cedejcapmas.org">https://www.cedejcapmas.org</a>   The other 3 shapefiles are: NILE for the Nile River layer EGYPT for the governorate/muhafaza level layer EGYPT_GOV for the district-level layer   The dataset is notably in the special issue of the Journal Geocarrefour (Revue de Géographie de Lyon), vol. 73, n°3, 1998 titled Géographie sociale de l'Egypte. See also:  <a href="https://www.jstor.org/stable/44381341">Denis, E. (1998). Croissance urbaine et dynamique socio-spatiale Le Caire de 1950 à 1990. L'espace géographique, 129-142.</a> Denis, É., & Moriconi-Ébrard, F. (1998). La population de l'Egypte 1897-1996. L'information Géographique, 62(1), 12-23.     1.      S. Fanchette. 1997. Le delta du Nil. Densités de population et urbanisation des campagnes. Fascicule de Recherches n°32. Urbama-Orstom. Tours. 389 p.

opencc-by-4.0Jan 2023View details →

National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets

Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information. Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.

opencc-by-4.0Jan 2023View details →

"Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" - calcareous nannofossil census data

This is a data supplement (Dataset A) to the paper "Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" by Bordiga et al., 2015a (https://doi:10.5194/cp-11-1249-2015). Note that data are tabulated against depth in core (meters composite depth, mcd). Please refer to Table 1 in Bordiga et al. (2015) for age-depth model. Dataset A. Calcareous nannofossil census data (ODP Site 1263) One file (ODP 1263 dataset A_Bordiga et al. 2015.xls) containing: Sample information; Raw counts, relative (%) and absolute abundances (N/ g) of all species and size-based groups detected (as illustrated in Figure S2 of the original publication).

opencc-zeroMar 2023View details →

Measures of urban form and mobility energy use indices for each census tract in the United States

This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form.

opencc-by-4.0Apr 2023View details →

Statistics of difference product by census tracts and metropolitan statistical areas

Statistics of difference product by census tracts:  Statistics_by_census_tracts.shp Statistics of difference product by Metropolitan Statistical Areas: Statistics_by_Metropolitan_Statistical_Areas.shp

opencc-zeroJun 2023View details →

Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama

Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50-ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-yr intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m2 seedling plot in the center of every 5 x 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m2 seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1–2 years. Here we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography.

opencc-zeroSep 2023View details →

Soil and census data of Heishiding plot

Intransitive competition has long been acknowledged as a potential mechanism favoring species coexistence. However, its prevalence, variance along environmental gradients, and possible underlying mechanisms (trade-offs) in plant communities (especially in forests) has seldomly been examined. A recently developed "reverse-engineering" approach based on Markov Chain allows us to estimate competitive transition matrices and competitive intransitivity from observational abundance data. Using this approach, we estimated competitive intransitivity of five dominant species in a subtropical forest and then related it to soil fertility (soil organic matter and soil pH) and demographic trade-offs (growth-survival and stature-recruitment trade-offs). In our forest plot, intransitive competition was common among the dominant species and peaked at the intermediate level of soil organic matter. Neither the growth-survival trade-off nor the stature-recruitment trade-off was positively related to competitive intransitivity. Our study for the first time empirically supported the unimodal intransitivity-fertility relationship in forests, which, however, was not mediated by the two demographic trade-offs in our plot.

opencc-by-4.0Sep 2023View details →

Predicting school uptake of The Daily Mile in Northern Ireland- a data linkage study with School Census Data and Multiple Deprivation Measures

The datasets for the research into the uptake of The Daily Mile in Northern Ireland- a data linkage study with School Census Data and Multiple Deprivation Measures. Questionnaires completed between 31st August 2022 and 16th December 2022.

publicMar 2018View details →

Tree species abundance through time in tropical forest census plots, Panama

Open the record for dataset details and reuse information.

publicJun 2023View details →

Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama

Open the record for dataset details and reuse information.