Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Underwater images collected by Scuba diving in Ifaty, Madagascar - 2023-05-07

This dataset was collected by Scuba diving in Ifaty, Madagascar - 2023-05-07. Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 2.66 GB of MP4 files, which were trimmed into 1056 frames (at 2997/1000 fps). The frames are not georeferenced. 93.75% of these extracted images are useful and 6.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM : folder to store videos and photos depending on the media collected. ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. │ ├── BASE : files coming from rtk station or any static positioning instrument. │ └── DEVICE : files coming from the device. ├── METADATA : folder with general information files about the session. ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

opencc-by-4.0May 2024View details →

Underwater images collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30

This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30. Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 1.5 GB of JPG files, which were trimmed into 267 frames (at 4 fps). The frames are georeferenced. 99.63% of these extracted images are useful and 0.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM : folder to store videos and photos depending on the media collected. ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. │ ├── BASE : files coming from rtk station or any static positioning instrument. │ └── DEVICE : files coming from the device. ├── METADATA : folder with general information files about the session. ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

opencc-by-4.0May 2024View details →

Underwater images collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30

This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30. Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 2.49 GB of JPG files, which were trimmed into 460 frames (at 4 fps). The frames are not georeferenced. 100.0% of these extracted images are useful and 0.0% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM : folder to store videos and photos depending on the media collected. ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. │ ├── BASE : files coming from rtk station or any static positioning instrument. │ └── DEVICE : files coming from the device. ├── METADATA : folder with general information files about the session. ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

opencc-by-4.0Apr 2024View details →

Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"

This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)

opencc-by-4.0Oct 2024View details →

Deep Submergence Dive location dataset from Bell et al. Sci Advances: How Little We've Seen: A Visual Coverage Estimate of the Deep Seafloor

How Little We’ve Seen: A Visual Coverage Estimate of the Deep Seafloor  Katherine L.C. Bell,1&lowast; Kristen N. Johannes,1,2  Brian R.C. Kennedy,1,3 Susan E. Poulton1  1Ocean Discovery League, Saunderstown, RI 02874, USA,  2Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, San Diego, CA 92037, USA  3Biology Department, Boston University, Boston, MA 02215 USA  &lowast;To whom correspondence should be addressed: croff@alum.mit.edu.  Despite the importance of visual observation in the ocean, we have imaged a minuscule fraction of the deep seafloor. Sixty-six percent of the entire planet is deep ocean (≥200 m), and our data show we have visually observed less than 0.001%, a total area approximately a tenth of the size of Belgium. Data gathered from over 44 thousand deep-sea dives indicate we have also seen an incredibly biased sample. Sixty-five percent of all in situ visual seafloor observations in our dataset were within 200 nm of only three countries: the United States, Japan, and New Zealand. Ninety-seven percent of all dives we compiled have been conducted by just five countries: the United States, Japan, New Zealand, France, and Germany. This small and biased sample is problematic when attempting to characterize, understand, and manage a global ocean.

opencc-by-4.0May 2020View details →

Weddell seal diving data from the Weddell Sea (2011)

These data comprise locations and dive metrics from 19 adult Weddell seals (Leptonychotes weddellii) instrumented in February 2011 with telemetry devices. These devices were Sea Mammal Research Unit Conductivity-Temperature-Depth Satellite Relay Data Loggers. These are animal-attached loggers that are manufactured and programmed by the Sea Mammal Research Unit Instrumentation Group at the University of St Andrews, Scotland. Ethical approval for this work was obtained from the University of St Andrews Animal Welfare and Ethics Committee, and from the corresponding committee at the British Antarctic Survey. This dataset is linked to the manuscript Photopoulou et al. 2020 "Sex-specific variation in the use of vertical habitat by a resident Antarctic top predator" Proceedings of the Royal Society B (http://dx.doi.org/10.1098/rspb.2020.1447) and a preprint on bioRxiv (https://doi.org/10.1101/2020.06.15.152009). The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows. ID: This a factor variable identifying each individual seal. It has 19 levels. sex: This is a character variable identifying the sex of the seal, either "F" or "M" for female and male. source: This is a factor variable identifying the type of record. It has three levels "dv", "ho" and "sf" for dive, haulout and surface record respectively.  start_date: This is a date variable (POSIXct) that gives the date and time of the beginning of an observation in UTC time. end_date: This is a date variable (POSIXct) that gives the date and time of the end of an observation in UTC time. local_start_date: This is a date variable (POSIXct) that gives the local date and time of the beginning of an observation at the geographic location of the seal. end_end_date: This is a date variable (POSIXct) that gives the local date and time of the end of an observation at the geographic location of the seal. fg_lon: This is a numeric variable and gives the longitude of the seal at the local end time of each record. This is the product of fitting a statistical model to the observed ARGOS location data, taking account of the error class. This is not the originally observed location. The model used was a continuous time state-space model implemented in the R package foieGras by Ian Jonsen and Toby Patterson (2019) foieGras: Fit Continuous-Time State-Space and Latent Variable Models for Filtering Argos Satellite (and Other) Telemetry Data and Estimating Movement Behaviour. R package version 0.4.01. <https://cran.r-project.org/package=foieGras> fg_lat: This is a numeric variable and gives the latitude of the seal at the local end time of each record. This is the product of fitting a statistical model to the observed ARGOS location data, taking account of the error class. This is not the originally observed location. The model used was a continuous time state-space model implemented in the R package foieGras by Ian Jonsen and Toby Patterson (2019) as above in fg_lon. hour: This is a positive integer variable giving the hour of the day at the local end date. week: This is a positive integer variable giving week of the year at the UTC end date. DURATION: This is a positive integer variable that gives the duration of the record in seconds. MAX_DEP: This is a positive continuous variable that gives the maximum depth (m) visited during a record. This is only zero for haulout records. hunt_secs: This is a positive integer variable that gives the total number of seconds spent in hunting behaviour as calculated using methods developed by Heerah et al. (2019) Validation of dive foraging indices using archived and transmitted acceleration data: The case of the Weddell seal. Frontiers in Ecology and Evolution, 7: 30 and in the supplementary material of Photopoulou et al. (2020) Sex-dependent variation in the use of vertical habitat by a resident Antarctic top predator.  hunt_prop: This is a numeric variable ranging from 0 to 1. It represents the proportion of dive time spent hunting and is calculated as hunt_secs/DURATION for dive records only. hunt_avgdep: This is a positive continuous variable giving the mean depth (m) of hunting behaviour as calculated using methods described above. hunt_mindep: This is a positive continuous variable giving the minimum (shallowest) depth (m) of hunting behaviour, as calculated using methods described above. hunt_maxdep: This is a positive continuous variable giving the maximum (deepest) depth (m) of hunting behaviour, as calculated using methods described above. bathy: This is a negative continuous variable giving the depth (m) of the seal floor at the record's location based on the IBSCO bathymetry dataset Arndt et al. (2013) The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0—A new bathymetric compilation covering circum‐Antarctic waters. Geophysical Research Letters, 40(12): 3111-3117  bathy_std: This is a numeric variable giving the standardised bathymetry value calculated as (bathy-mean(bathy))/sd(bathy). prop_bathy: This is a numeric variable ranging from 0 to 1, giving the proportion of bathymetry reached at the maximum dive depth, calculated as MAX_DEP/bathy. prop_hunt_bathy: This is a numeric variable ranging from 0 to 1, giving the proportion of bathymetry reached at the mean hunting depth, calculated as hunt_avgdep/bathy. dep4ctd: This is a discrete numeric variable diving the depth at which temperature and salinity were extracted from the CTD profile nearest in time to the location of the record. The CTD profile was interpolated at a 10m resolution. psal: This is a positive continuous variable giving the salinity (PSU) at the location of the record, at the depth given by dep4ctd. When there was no hunting, there is an NA. temp: This is a positive continuous variable giving the potential temperature (degrees Celsius) at the location of the record, at the depth given by dep4ctd. When there was no hunting, there is an NA.

Figs 12–16 in New records of Platynectes (s. str.) diving beetles from South America with description of two new species (Coleoptera: Dytiscidae: Agabinae)

Figs 12–16. Distribution of Platynectes 'Clade II' sensu TƟUඌඌΑιΝτ et al. (2017) in South America. Circles represent previously published localities; stars represent localities newly reported in this paper.

Figs 2–5 in New records of Platynectes (s. str.) diving beetles from South America with description of two new species (Coleoptera: Dytiscidae: Agabinae)

Figs 2–5. Dorsal habitus images of Platynectes spp. 2 – P. bicolor sp. nov.; 3 – P. colombicus sp. nov.; 4 – P. ornatifrons Sharp, 1882, syntype; 5 – P. parananus Sharp, 1882, holotype.

Figs 6–9 in New records of Platynectes (s. str.) diving beetles from South America with description of two new species (Coleoptera: Dytiscidae: Agabinae)

Figs 6–9. Median lobes of the adeagus. 6–7 – P. bicolor sp. nov.: 6 – left lateral view; 7 – right lateral view. 8–9 – P. colombicus sp. nov.: 8 – left lateral view; 9 – right lateral view. Scale bars = 0.5 mm.

Fig. 1 in New records of Platynectes (s. str.) diving beetles from South America with description of two new species (Coleoptera: Dytiscidae: Agabinae)

Fig. 1. Maximum Likelihood phylogeny of sequenced Platynectes (s. str.) specimens based on the gene COI.

opencc-zeroDec 2023View details →

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Diving behaviors of juvenile northern and southern elephant seals

Understanding the ontogeny of diving behavior in marine megafauna is crucial due to its influence on foraging success, energy budgets, and mortality. We compared the ontogeny of diving behavior in two closely related species – northern elephant seals (Mirounga angustirostris, n = 4) and southern elephant seals (Mirounga leonina, n = 9) – to shed light on the ecological and evolutionary processes underlying migration. Although both species have similar sizes and behaviors as adults, we discovered that juvenile northern elephant seals have superior diving development, reaching 260 meters diving depth in just 30 days, while southern elephant seals require 160 days. Similarly, northern elephant seals achieve dive durations of ~11 minutes on their first day of migration, while southern elephant seals take 125 days. The faster physiological maturation of northern elephant seals could be related to longer offspring dependency and post-weaning fast durations, allowing them to develop their endogenous oxygen stores. Comparison across both species suggests that weaned seal pups face a trade-off between leaving early with higher energy stores but poorer physiological abilities or leaving later with improved physiology but reduced fat stores. This trade-off might be influenced by their evolutionary history, which shapes their migration behaviors in changing environments over time.

opencc-by-4.0Mar 2024View details →

Abdominal colour patterns of the sand diving spider Ammoxenus amphalodes (Araneae: Gnaphosidae) from South Africa

The two types of abdominal patterns found in Ammoxenus species are discussed, with emphasis on A. amphalodes Dippenaar & Meyer, 1980. With images of live specimens, the two patterns are shown. Within the genus, there is large interspecific similarity, but intraspecific variability regarding the abdominal colour pattern. Due to these variations found species are sometimes wrongly identified.

F I G U R E 3 in Phylogenetic placement and description of Ngaliadessus humphreysi gen. et sp. nov., Watts & Villastrigo (Coleoptera: Dytiscidae), a subterranean diving beetle from the Ngalia Basin in central Australia

F I G U R E 3 Adult Ngaliadessus humphreysi sp. nov., Watts & Villastrigo. (a) Dorsal view of adult; (b) line drawing; (c) line drawing of ventral surface of mesotrochanter and mesofemur; (d) line drawing of ventral surface of metatrochanter and metafemur; (e) portion of spermatheca with internal spine.

F I G U R E 2 in Phylogenetic placement and description of Ngaliadessus humphreysi gen. et sp. nov., Watts & Villastrigo (Coleoptera: Dytiscidae), a subterranean diving beetle from the Ngalia Basin in central Australia

F I G U R E 2 BEAST maximum clade credibility tree of Bidessini based on a combination of whole mitochondrial genome sequences, Histone 3 and 18S sequences (Table S1). The new genus/species is shown in bold, and numbers on branches represent Bayesian posterior probabilities.

F I G U R E 1 A in Phylogenetic placement and description of Ngaliadessus humphreysi gen. et sp. nov., Watts & Villastrigo (Coleoptera: Dytiscidae), a subterranean diving beetle from the Ngalia Basin in central Australia

F I G U R E 1 A geological representation of the Ngalia Basin with subterranean groundwaters and its main sampling sites for stygobiotic taxa, including Sullivans Well. The geology was determined from Australian Geological Survey 1: 250 000 maps SF52-12 Mt Doreen (1995) and Napperby SF53-09 (1982) and information compiled by Edgoose and Ahmad (2013). Inset shows the approximate location of the Ngalia Basin in central Australia.

F I G U R E 4 in Phylogenetic placement and description of Ngaliadessus humphreysi gen. et sp. nov., Watts & Villastrigo (Coleoptera: Dytiscidae), a subterranean diving beetle from the Ngalia Basin in central Australia

F I G U R E 4 Line drawings of larva of Ngaliadessus humphreysi sp. nov.: (a) ventral view of head capsule; (b) dorsal surface of head capsule; (c) enlarged view of ventral surface of head capsule; (d) dorsal view of abdominal segment 8 and basal segment of urogomphi; small circle on segment 8 represents only visible pore.

Figure 12 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 12. Phylomorphospace of the principal components 1 and 2 for the forelimb (a) and its correlation circle (b). The dotted circle delimits the Ziphiidae family. The branches represent the phylogenetic relationships between the different species. The abbreviations are the same as in Fig. 11 except for the following: Dele: Delphinapterus leucas; Doat: Dorudon atrox; Mebo:Mesoplodon bowdoini; Oror: Orcinus orca; Stco: Stenella commersonii.

Figure 11 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 11. Phylomorphospace of the principal components 1 and 2 for the hamular fossa of the pterygoid sinus (a) and its correlation circle (b). The dotted circle delimits the Ziphiidae family. The branches represent the phylogenetic relationships between the different species. Abbreviations: Ceco: Cephalorhynchus commersonii; Glme: Globicephala melas; Grgr: Grampus griseus; Hyam: Hyperoodon ampullatus; Laac: Lagenorhynchus acutus; Mebi: Mesoplodon bidens; Megr: Mesoplodon grayi; Megre: Messapicetus gregarius; Mepe: Mesoplodon peruvianus; Momo: Monodon monoceros; Orbr:Orcaella brevirostris; Phma: Physeter macrocephalus; Pobl: Pontoporia blainvillei; Plga: Platanista gangetica; Pscr: Pseudorca crassidens; Saob:Sagmatias obscurus; Sofl: Sotalia fluviatilis; Stat: Stenella attenuata; Stcl: Stenella clymene; Stfr: Stenella frontalis; Tutr: Tursiops truncatus; Zica: Ziphius cavirostris.

Figure 7 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 7. Humeri and left radius of MUSM 2542, Messapicetus gregarius. Right humerus in medial (a), lateral (b), ulnar (c), and radial view (d); left humerus in medial (e), lateral (f), ulnar (g), radial (h), and posterior view (i); left radius in lateral (j), medial (k), and ulnar view (l).

Figure 5 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 5. Ribs of the specimen MUSM 2548, Messapicetus gregarius, in anterior view. Pair 1 (a) and (b); pair 2 (c) and (d); pair 3 (e) and (f); pair 4 (g) and (h); (i), (j), (k), and (l) cannot be precisely positioned.