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319 results for “population estimates”
Production, biomass, and yield estimates for walleye populations in the Ceded Territory of Wisconsin from 1990-2017
Recreational fisheries are valued at $190B globally and constitute the predominant use of wild fish stocks in developed countries, with inland systems contributing the dominant fraction of recreational fisheries. Although inland recreational fisheries are thought to be highly resilient and self-regulating, the rapid pace of environmental change is increasing the vulnerability of these fisheries to overharvest and collapse. We evaluate an approach for detecting hidden overharvest of inland recreational fisheries based on empirical comparisons of harvest and biomass production. Using an extensive 28-year dataset of the walleye fisheries in Northern Wisconsin, USA, we compare empirical biomass harvest (Y) and calculated production (P) and biomass (B) for 390 lake-year combinations. Overharvest occurs when harvest exceeds production in that year. Biomass and biomass turnover (P/B) both declined by about 30% and about 20% over time while biomass harvest did not change, causing overharvest to increase. Our analysis revealed 40% of populations were production-overharvested, a rate about 10x higher than current estimates based on numerical harvest used by fisheries managers. Our study highlights the need for novel approaches to evaluate and conserve inland fisheries in the face of global change.
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate
<p>1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.</p> <p> </p> <p>How to use</p> <pre>from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5))</pre> <p> </p> <p>Files:</p> <ul> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/towns.csv">towns.csv</a> - city information</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/regions.csv">regions.csv</a> - list of Russian Federation regions</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/alt_city_names.json">alt_city_names.json</a> - alternative city names</li> </ul> <p> </p> <p>Сolumns (towns.csv):</p> <p>Basic info:</p> <ul> <li><code>city</code> - city name (several cities have alternative names marked in <code>alt_city_names.json</code>)</li> <li><code>population</code> - city population, thousand people, Rosstat estimate as of 1.1.2020</li> <li><code>lat,lon</code> - city geographic coordinates</li> </ul> <p>Region:</p> <ul> <li><code>region_name</code> - subnational region (oblast, republic, krai or AO)</li> <li><code>region_iso_code</code> - <a href="https://en.wikipedia.org/wiki/ISO_3166-2:RU">ISO 3166 code</a>, eg <code>RU-VLD</code></li> <li><code>federal_district</code>, eg <code>Центральный</code></li> </ul> <p>City codes:</p> <ul> <li><code>okato</code></li> <li><code>oktmo</code></li> <li><code>fias_id</code></li> <li><code>kladr_id</code></li> </ul> <p> </p> <p>Data sources</p> <ul> <li>City list and city population collected from Rosstat publication <a href="https://rosstat.gov.ru/folder/210/document/13206">Регионы России. Основные социально-экономические показатели городов</a> and parsed from publication Microsoft Word files.</li> <li>City list corresponds to <a href="https://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D0%BE%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8">this Wikipedia article</a>.</li> <li>Alternative dataset is <a href="https://github.com/hflabs/city">wiki-based Dadata city dataset</a> (no population data).</li> </ul> <p> </p> <p>Comments</p> <p> </p> <p>City groups</p> <ul> <li> <p><code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication as parts of <code>Тюменская область</code>.</p> </li> <li> <p>Several notable towns are classified as administrative part of larger cities (<code>Сестрорецк</code> is a municpality at Saint-Petersburg, <code>Щербинка</code> part of Moscow). They are not and not reported in this dataset.</p> </li> </ul> <p> </p> <p>By individual city</p> <ul> <li><code>Белоозерский</code> not found in Rosstat publication, but <a href="https://github.com/epogrebnyak/ru-cities/issues/5#issuecomment-886179980">should be considered a city as of 1.1.2020</a></li> </ul> <p> </p> <p>Alternative city names</p> <ul> <li> <p>We suppressed letter "ё" <code>city</code> columns in towns.csv - we have <code>Орел</code>, but not <code>Орёл</code>. This affected:</p> <ul> <li><code>Белоозёрский</code></li> <li><code>Королёв</code></li> <li><code>Ликино-Дулёво</code></li> <li><code>Озёры</code></li> <li><code>Щёлково</code></li> <li><code>Орёл</code></li> </ul> </li> <li> <p><code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city.</p> </li> </ul> <p><code>assets/alt_city_names.json</code> contains these names.</p> <p> </p> <p>Tests</p> <pre><code>poetry install poetry run python -m pytest </code></pre> <p> </p> <p>How to replicate dataset</p> <p> </p> <p>1. Base dataset</p> <p>Run:</p> <ul> <li>download data stro rar/get.sh</li> <li>convert <code>Саратовская область.doc</code> to docx</li> <li>run make.py</li> </ul> <p>Creates:</p> <ul> <li><code>_towns.csv</code></li> <li><code>assets/regions.csv</code></li> </ul> <p> </p> <p>2. API calls</p> <p>Note: do not attempt if you do not have to - this runs a while and loads third-party API access.</p> <p>You have the resulting files in repo, so probably does not need to these scripts.</p> <p>Run:</p> <ul> <li><code>cd geocoding</code></li> <li>run coord_dadata.py (needs token)</li> <li>run coord_osm.py</li> </ul> <p>Creates:</p> <ul> <li>coord_dadata.csv</li> <li>coord_osm.csv</li> </ul> <p> </p> <p>3. Merge data</p> <p>Run:</p> <ul> <li>run merge.py</li> </ul> <p>Creates:</p> <ul> <li>assets/towns.csv</li> </ul> <p> </p>
Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps
<p><strong>The dataset<br></strong></p> <p>The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.</p> <p>Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (<em>Bemisia tabaci</em>) (Gennadius) and the greenhouse whitefly (<em>Trialeurodes vaporariorum</em>) (Westwood).</p> <p>The dataset comprises two subsets:<br>- a subset we suggest using for the training/validation phases (contained in the `train/` folder)<br>- a subset we suggest using for the test phase (contained in the `test/` folder)</p> <p>Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:<br>- *imageName* - filename of the image containing the whiteflies,<br>- *X,Y* - 2D coordinates of the whitefly in the image space,<br>- *class* - class index of the insect (always 0 in this dataset).</p> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{CIAMPI2023102384,<br> title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},<br> journal = {Ecological Informatics},<br> volume = {78},<br> pages = {102384},<br> year = {2023},<br> issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {<a href="https://doi.org/10.5281/zenodo.7801239">https://doi.org/10.5281/zenodo.6560823</a>} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p> <p> </p>
Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs"
<p>Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs" (https://doi.org/10.5281/zenodo.7927968), recommended by PCI Evol Biol (https://evolbiol.peercommunityin.org/articles/rec?id=651)</p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
Baltimore Ecosystem Study: Estimates of population in focal watersheds based on 2010 census
This dataset includes population estimates for eight focal sub-watersheds in the Baltimore Ecosystem Study based on the proportion of 2010 census blocks located within the watershed. These data can facilitate per capita calculations of watershed fluxes.
Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic - Estimates for Local Authority Districts
<p><strong>Overview:</strong></p> <p>Population estimates from the publication: <em>Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.</em> </p> <p>Population estimates were aggregated to Local Authority Districts (LADs). </p> <p><strong>Methodology: </strong></p> <p>Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.</p> <p>2019 LAD boundaries are available from the <a href="https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bfc/explore">UK Government Open Geography Portal</a>.</p> <p> </p>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
A comparison of density estimation methods for monitoring marked and unmarked animal populations
<p>These data were generated to compare different methods of estimating population density from marked and unmarked animal populations. We compare conventional live trapping with two more modern, non-invasive field methods of population estimation: genetic fingerprinting from hair-tube sampling and camera trapping for the European pine marten (Martes martes). We used arrays of camera traps, live traps, and hair tubes to collect the relevant data in the Ring of Gullion in Northern Ireland. We apply marked spatial capture-recapture models to the genetic and live trapping data where individuals were identifiable, and unmarked spatial capture-recapture (uSCR), distance sampling (CT-DS), and random encounter models (REM) to the camera trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.41 – 0.99 animals per km<sup>2</sup>), despite differences in precision, cost, and effort being apparent.</p> <p>In addition to the data, we provide novel code for running unmarked spatial capture-recapture (uSCR) and random encounter models (REM) to the camera trap data where individual ID was not possible. </p>
Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Phocoena phocoena) population using passive acoustic monitoring
<p>Knowing the abundance of a population is a crucial component to assess its conservation status and develop effective conservation plans. For most cetaceans, abundance estimation is difficult given their cryptic and mobile nature, especially when the population is small and has a transnational distribution. In the Baltic Sea, the number of harbour porpoises (<i>Phocoena phocoena</i>) has collapsed since the mid-20<sup>th</sup> century and the Baltic Proper harbour porpoise is listed as Critically Endangered by the IUCN and HELCOM; however, its abundance remains unknown. Here, one of the largest ever passive acoustic monitoring studies was carried out by eight Baltic Sea nations to estimate the abundance of the Baltic Proper harbour porpoise for the first time. By logging porpoise echolocation signals at 298 stations during May 2011-April 2013, calibrating the loggers' spatial detection performance at sea, and measuring the click rate of tagged individuals, we estimated an abundance of 71-1,105 individuals (95% CI, point estimate 491) during May-October within the population's proposed management border. The small abundance estimate strongly supports that the Baltic Proper harbour porpoise is facing an extremely high risk of extinction, and highlights the need for immediate and efficient conservation actions through international cooperation. It also provides a starting point in monitoring the trend of the population abundance to evaluate the effectiveness of management measures and determine its interactions with the larger neighbouring Belt Sea population. Further, we offer evidence that design-based passive acoustic monitoring can generate reliable estimates of the abundance of rare and cryptic animal populations across large spatial scales.</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Figure S1 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure S1. Snail counts (y axis) at sites A (above) and B (below) for the three cohorts showing unmarked and recaptures separately for each visit: juveniles too small to mark, juveniles newly dotted, dotted juveniles recaptured, newly marked adult and subadult individuals, already marked adult and subadult individuals (recaptures).
Figure 9 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 9. Temperature and humidity profiles at sites A, B, and C between April and December 2017. TA = temperature at site A (three loggers), TB = temperature at site B (three loggers), TC = temperature at site C (three loggers). HA, HB, HC = humidity profile at sites A, B, and C, respectively. Some missing data are due to temporal failure of some loggers. Humidity loggers stopped to function completely in the middle of October at all three sites.
Figure 7 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 7. Minimum distances (in meters, x axis) of numbered individuals (y axis: absolute numbers of individuals) from sites A and B, over all subsequent observations.
Figure 6 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 6. Minimum distances (in meters, x axis) of numbered individuals (y axis: absolute numbers of individuals) from site A and B of all observations with a ~1-week interval.
Figure 3 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 3. Snail counts at site A (above) and site B (below); juv. small = small juveniles (too small to mark); juv. dotted = dotted individuals; subadults and adults = numbered subadults/adults.
Figure 8 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 8. Sum of MDs (in meters, x axis) over all subsequent observations (over the season) of numbered individuals (y axis: absolute numbers of individuals) from sites A and B.
Figure 2. M in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 2. M. subcristata shell-size classes: (A) small juveniles (too small to mark), appr. 8 mm shell height), (B) dotted juvenile, appr. 12 mm; (C) numbered adult, appr. 20 mm.
Figure 5 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 5. Population estimated according to Jolly-Seber for site A (above), site B (below). Y axis: estimated number of individuals.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.