Tracking an animal's location from video has many applications, from providing information on health and welfare to validating sensor-based technologies. Typically, accurate location estimation from video is achieved using cameras with overhead (top-down) views, but structural and financial limitations may require mounting cameras at other angles. We describe a user-friendly solution to manually extract an animal's location from non-overhead video. Our method uses QGIS, an open-source geographic information system, to: (1) assign facility-based coordinates to pixel coordinates in non-overhead frames; 2) use the referenced coordinates to transform the non-overhead frames to an overhead view; and 3) determine facility-based x, y coordinates of animals from the transformed frames. Using this method, we could determine an object's facility-based x, y coordinates with an accuracy of 0.13 ± 0.09 m (mean ± SD; range: 0.01–0.47 m) when compared to the ground truth (coordinates manually recorded with a laser tape measurer). We demonstrate how this method can be used to answer research questions about space-use behaviors in captive animals, using 6 ewe-lamb pairs housed in a group pen. As predicted, we found that lambs maintained closer proximity to their dam compared to other ewes in the group and lamb-dam range sizes were strongly correlated. However, the distance traveled by lambs and their dams did not correlate, suggesting that activity levels differed within the pair. This method demonstrates how user-friendly, open-source GIS tools can be used to accurately estimate animal location and derive space-use behaviors from non-overhead video frames. This method will expand capacity to obtain spatial data from animals in facilities where it is not possible to mount cameras overhead.

Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.

Georeferencing the "Atlas du plan général de la ville de Paris par Edme Verniquet" Géoréférencement de l'Atlas du plan général de la ville de Paris par Edme Verniquet This dataset contains the necessary data control points to georeference the "Atlas du plan général de la ville de Paris par Edme Verniquet" based on 2 different versions of the atlas: one digitized by the Bibliothèque nationale de France (BnF) and the other by The David Rumsey Historical Map Collection. The dataset contains the control points in QGIS format (.points files) and as Allmaps georeference annotations. It also contains the georeferenced map sheets as geotiff.

This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

Primary biodiversity records were queried from the Global Biodiversity Information Facility on January 30 and May 10, 2021 for plants (Plantae; https://doi.org/10.15468/dl.th5tn8; https://doi.org/10.15468/dl.76jc24), June 3, 2022 for birds (Aves; https://doi.org/10.15468/dl.jh3u2u), and August 23, 2021 for insects (Insecta; https://doi.org/10.15468/dl.4q2972), and mammals (Mammalia; https://doi.org/10.15468/dl.cujmgz). We then assessed the frequency of the geographic coordinates and identified the most frequently recurring sets of coordinates across each taxonomic group. Coordinates were assessed as provided in the “decimalLatitude” and “decimalLongitude” columns of the downloaded data without any rounding to be conservative. Rounding coordinates before assessing their frequency would increase the overall number of records associated with each set of coordinates and increase the risk of associating true points with georeferenced ones. Only exact matches were counted to calculate the frequency of each unique set of coordinates. We determined which of the highly-recurrent coordinates are likely artificial by examining metadata and images from datasets comprising over 40 million records to date; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature (Fig. 2). We used QGIS software to validate grid centroid coordinates by plotting the grid systems over the reported occurrence coordinates to confirm the grid centroid, grid size and the coordinate reference system. Countries represented in our dataset that utilized such grids were identified through occurrence record metadata, visual inspection of associated datasets, literature review, and data managers, and included France, the United Kingdom, Germany, the Netherlands, Belgium, Switzerland, and Spain. For each group, we started by evaluating the most recurrent set of coordinates and proceeded in order of decreasing frequency. We initially examined the top 100 recurring coordinates for plants and the top 50 recurring coordinates for each animal group. These coordinates were manually curated into the following categories when possible: grid centroid, geopolitical centroid, georeferenced location, and true observation or collection site. Some coordinates could be associated with multiple categories. It is possible that the determinations we made for highly-recurrent coordinates could also be extended to additional, less recurrent, coordinates that were assigned to other records in the datasets they belonged to (but not included in our initial survey). These data were compiled into AHOI, an inventory of highly-recurrent GBIF coordinates, with their descriptions and determinations. To validate our approach and assess whether artificial biodiversity hotspots are the result of systemic practices or errors, we additionally evaluated data from the Field Museum of Natural History, as some of the top 100 most recurring coordinates were associated with the institution. We downloaded all plant records from this dataset and evaluated all coordinates that were assigned to at least 1000 records. We found that the coordinates from this dataset represented artificial aggregates of specimens around geopolitical centroids. These verifications were also included in AHOI. Further, we listed the rationale for each individual coordinate determination and provides examples of relevant information from occurrence record metadata in the “example_description” and “reasoning” fields respectively. Aim: Species occurrence records are essential to understanding Earth’s biodiversity and addressing global environmental issues, but do not always reflect actual locations of occurrence. Certain geographic coordinates are assigned repeatedly to thousands of observation/collection records. This may result from imperfect data management and georeferencing practices, and can greatly bias the inferred distribution of biodiversity and associated environmental conditions. Nonetheless, these ‘biodiverse’ coordinates are often overlooked in taxon-centric studies, as they are identifiable only in aggregate across taxa and datasets, and it is difficult to determine their true circumstance without in-depth, focused investigation. Here we assess highly recurring coordinates in biodiversity data to determine artificial hotspots of occurrences. Location: Global Taxon: Land plants, birds, mammals, insects Methods: We identified highly recurring coordinates across plant, bird, insect, and mammal records in the Global Biodiversity Information Facility, the largest aggregator of biodiversity data. We determined which are likely artificial hotspots by examining metadata from over 40 million records; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature. These results were compiled into the Artificial Hotspot Occurrence Inventory (AHOI). Results...

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

• Freshwater Fishes and Lampreys of Greece: An Annotated Checklist
• The Red Book of Endangered Animals of Greece
• The "Red List of Threatened Species"
• The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"
• The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.