The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

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.

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 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.

Overview

This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a collaborative drone campaign organized by the Urban Transport Systems Laboratory (LUTS), EPFL, within the framework of NCCR Automation, in cooperation with the cantonal traffic planning department of Aargau. The collection took place on Monday, 23rd October 2023, during peak morning and afternoon hours, resulting in nearly 11 hours of 4K video data.

Dataset Composition

This dataset contains CSV files structured with consistent data fields representing georeferenced trajectories, vehicle types (car, bus, truck), and timestamps, capturing detailed vehicle movements within roundabout environments.

File Organization

File names follow the convention:

D{X}_{TP}{N}_{S}.csv

D{X} — the drone identifier, where {X} is a number (e.g., 1, 2) indicating which drone captured the data.→ Example: D1 = data collected by Drone 1.

{TP}{N} — the time period and session number, where {TP} is either AM (morning) or PM (afternoon), and {N} is an integer indicating the session number.→ Example: AM2 = second morning session.

{S} — the site identifier, corresponding to one of the monitored sites:→ F1 = Roundabout F1 (Frick)→ F2 = Roundabout F2 (Frick)→ L1 = Roundabout L1 (Laufenburg)

CSV File Structure

Each CSV file includes:

Column Name Description Format / Units

track_id Unique vehicle identifier (per file) Integer

type Vehicle type (Car, Bus, Truck) Categorical

lon WGS84 geographic longitude Decimal degrees (15 d.p.)

lat WGS84 geographic latitude Decimal degrees (15 d.p.)

time Local timestamp in ISO 8601 format String (hh:mm:ss.ss)

Data Collection and Processing

Collection Method: Two drones flying at an altitude of 120 meters above ground level, capturing videos at 4K resolution (3840×2160 pixels) at 29.97 FPS.

Locations:

Roundabout F1 (Frick): Intersection of Bahnhofstrasse and Hauptstrasse 3 (Urban)

Roundabout F2 (Frick): Intersection of Hauptstrasse 3 with Gänsacker and Stöcklimattstrasse (Urban)

Roundabout L1 (Laufenburg): Intersection at Hauptstrasse 7 near the German border (Rural)

Data Processing: The detection, tracking, and trajectory stabilization were performed using the early version of the Geo-trax framework (v0.1.0), an advanced computer vision pipeline tailored for drone-captured traffic footage. The resulting trajectories are precisely represented in stabilized pixel coordinates, which are subsequently transformed into geographic coordinates (WGS84). This georeferencing process follows a procedure similar to that described in Espadaler-Clapés et al., 2023, and includes:

Identification and extraction of Ground Control Points (GCPs) in the first stabilized video frame using QGIS Georeferencer, linking pixel coordinates to UTM coordinates.

Linear regression modeling between stabilized pixel coordinates and corresponding UTM coordinates derived from GCPs to estimate transformation parameters.

Projection to WGS84, converting UTM coordinates into global geographic coordinates using a standard GIS transformation (EPSG:4326).

Dataset Statistics

Roundabout Videos Avg. Duration (min) Total Duration (min) Vehicles (total) Cars Buses Trucks

F1 8 18.63 149.04 4,283 3,967 72 244

F2 6 19.24 115.44 2,528 2,205 26 297

L1 4 20.39 81.56 2,130 1,980 24 126

Potential Applications

This dataset is well-suited for:

Gap acceptance behavior studies at roundabouts (e.g., Pascual Anglès et al., 2025)

Traffic flow analysis and modeling

Safety assessments using surrogate safety measures (SSMs)

Validation of traffic simulation models

This collection contains georeferenced raster images (in GeoTIFF format) of the 'Gewestkaarten' (Regional maps) associated with Jacob van Deventer.

The georeferencing has been accomplished by linking hundreds of locality-pointers in the maps to the respective church towers. Using these pointers, the maps were distorted using Thin Plate Spline (Transformation method) / Nearest Neighbour (Resampling method).
Some regional maps, like the map of the County of Zeeland and surroundings, are so well executed that this method works really well without distorting the raster image too much. The map of the Duchy of Brabant (the oldest 'Gewestkaart' by Jacob van Deventer), is much less precise (with substantial intraregional differences). Only around half of the Brabant localities could be used as pointers in georeferencing in order to keep the distortion at an acceptable level.

The maps included in the dataset are:

• Jacob Bos, Flandriae recens exactaque descriptio (Rome: Michaelis Tramezini 1555). Based (partly?) on measurements by Jacob van Deventer and a map by Gerard Mercator, originally published around 1540. Amsterdam, Rijksmuseum, RP-P-BI-2756.
• Jacob Bos, Hollandiae Batavorum veteris insulae et locorum adiacentium exacta descriptio (Rome: Michaelis Tramezini 1556). Based on a map by Jacob van Deventer, originally published in 1542. Haarlem, Noord-Hollands Archief, NL-HlmNHA_560_000399_G.
• Jacob Bos, Brabantia Belgarum provinciae recens exactaque descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1536. Amsterdam, Rijksmuseum, RP-P-BI-2758. A different edition, similar to the lost Breslau edition of 1558 (link 1, 2, 3, 4), can be found in the Brussels Royal Library (circa 1556).
• Jacob Bos, Gelriae, Cliviae, Iuliae, nec non aliarum regionum adiacentium nova descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1543 (?). Amsterdam, Rijksmuseum, RP-P-BI-2757.
• Jacob Bos, Frisiae antiovissimae trans Rhenum provinc. et adiacentium regionum nova et exacta descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1545. Amsterdam, Rijksmuseum, RP-P-BI-2759.
• Jacob van Deventer, Zelandia (Antwerp: Gulielmus Sylvius 1560). Originally published in 1545 (or 1546?). Brielle, Streekarchief Voorne-Putten, 620 Collectie Topografische Kaarten, G058 ('TA_ALG_208 Kaart van Zeeland anno 1560. Komt voor een groot deel overeen met een kaart uit 1546 van Jacob van Deventer').

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

The data sets include 10 scanned historical maps (Plan 1-9 and 11) from 1920 of the region of Grosses Moos in Switzerland. The maps have been drawn by different surveyors with the plane table principle in 1920 represented with a map including topographical information such as contour lines, water bodies and houses and other urban infrastructures. In addition, the original measurement points were marked with a grid of 20-30m spacing and additional points if needed. These individual points were digitized by first georeferencing the individual maps with QGIS and then digitising each single measurement point along with their respective recorded heights. 44319 points were digites in LV03 (Swiss coordinate system) with the corresponding height (stored in the file "HoehendatenPunktwolke_Ins_1920.geojson").

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.

This dataset focuses on the historical mapping of the Greater Donaumoos fen region using old maps spanning the last 235 years. The main observations include the georeferencing of these historical maps and the subsequent vectorisation of the anthropogenic ditches and the Danube's surface area. The data collection encompasses maps spanning multiple centuries, providing temporal coverage that highlights landscape changes over significant historical periods. The data was collected to enhance archaeological, historical, and ecological research, offering insights into past landscapes and their transformations over time. The method involved digitising old maps and applying geospatial techniques to align them accurately with current geographical coordinates (Schmidt et al., 2024). This process was essential to create vector data representing the historical state of the ditches and the Danube river in this region. The purpose of this data collection is to provide a valuable resource for researchers studying historical land use, environmental changes, and regional development. The georeferencing and vectorisation processes were conducted using QGIS, ensuring precise alignment and accurate representation of historical features. The data generated from this project is crucial for understanding how the Greater Donaumoos fen region has evolved, offering a foundational dataset for further interdisciplinary studies.

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.

This dataset contains Light Detection and Ranging (LiDAR) elevation data from Quarantine Bay and Neptune Pass, located in southeastern Louisiana, USA, on the east side of the lower Mississippi River. Data were collected on 6 October 2022 and 28 March 2023 to investigate landform development and vegetation dynamics associated with the expansion of Neptune Pass, the largest new distributary of the Mississippi River. The study area spans from 29.397°N to 29.272°N latitude and from -89.521°W to -89.474°W longitude. Data acquisition was performed using a DJI Matrice 300 RTK drone platform equipped with a Zenmuse L1 gimbal payload, which integrates both a LiDAR scanner and a high-resolution RGB camera. Digital elevation models (DEMs) and visible light mosaics were produced from dense 3D point clouds using DJI Terra (https://enterprise.dji.com/dji-terra) and CloudCompare (https://github.com/CloudCompare/CloudCompare/releases/). Georeferencing was achieved using real-time kinematic (RTK) GPS ground control points in QGIS to align LiDAR pulses and imagery with real-world coordinates. The final products include high-resolution DEMs and RGB orthomosaics suitable for geomorphological and ecological analysis.

The folder contains 3 shapefiles usable in GIS (geographic information system). These data result from the processing of the french map of devastated regions ("carte des régions dévastées"). The map was edited in 1920 by the geographic service of French army. The objective was to classify lands depending on the intensity of destruction, and to locate areas where substantial restoration work was necessary. The 47 map sheets of the collection at scale 1:50,000 have been scanned and can be obtained from the National Geographic Institute (IGN) in .jpg format. The map shows large red-colored zones representing heavily damaged front-line area by trenches and bombing according to the map legend. There are also red-hatched features locating destroyed cities, roads and destroyed or cut forests. The blue-colored symbols show new constructions, such as memorials and cemeteries. For the methodology of georeferencing, classification and vectorization, see Nelly Paradelle, Marianne Laslier, Guillaume DeCocq, "Automatic extraction of former WWI battlefields from ancient maps," Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270H (17 October 2023); https://doi.org/10.1117/12.2684009 -MANUAL_ENVELOPE.shp : This dataset contains the envelope bordering the local destructions from the dataset "RED POLYGONS", and drawn manually within QGIS. -RED_POLYGONS.shp : This dataset contains only polygons of local destructions (cities, roads, buildings, destroyed or cut forests etc.) extracted from the map of devastated regions -RED_ZONE.shp : This dataset contains only polygons of the large red-colored areas representing heavily damaged front-line area by trenches and bombing extracted from the map of devastated regions Files with extension .qmd provide metadata.