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Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring
<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Original dataset on Forest Resilience Indicators and their compliance with Open data criteria)
<p>This dataset represents the original analysis on which my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor". </p> <ul> <li>The first sheet contains the coding on information systems on <strong>bird species occurrence</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The second sheet contains the coding on information systems on <strong>tree species distribution</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The third sheet contains the coding on information systems on <strong>soil water conditions</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fourth sheet contains the coding on information systems on <strong>canopy cover</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fifth sheet contains the coding on information systems on <strong>carbon sequestration</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The sixth sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The seventh sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages, ranked from highest to lowest compliance</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The last sheet gives <strong>information on the coding</strong>. </li> </ul>
Figure 1 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 1. Couple of Tyto furcata image captured by the security camera positioned opposite from the nest.Campos dos Goytacazes, RJ.
Figure 5 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 5. Day frequency that the Tyto furcata family brought food to the nest. Campos dos Goytacazes, RJ.
Figure 4 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 4. Frequency that the Tyto furcata parents bring the chicks near themselves (July and August, 2017). Campos dos Goytacazes, RJ.
Figure 6 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 6. Day frequency that the Tyto furcata family brought food to the nest, from laying the eggs until the chicks left the nest. Campos dos Goytacazes, RJ.
Figure 3 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 3. Observation of the Tyto furcata family in the nest. (A) Female sitting on eggs in the artificial nest; (B) 25-day-old chicks; (C) Adult owl bringing food to the chicks; (D) Chicks feeding alone in the nest. Campos dos Goytacazes, RJ.
Fig. 1 Mosquito collections 2011–2019 in Nine years of mosquito monitoring in Germany, 2011-2019, with an updated inventory of German culicid species
Fig. 1 Mosquito collections 2011–2019 (green dots—trap collections; red dots— 'Mueckenatlas' submissions; blue dots—netting, aspirating and baited collections of adults; yellow dots—dipping/sieving of immature developmental stages)
Fig 2 in Monitoring and assessing the physico-chemical water properties and planktonic communities in tilapia nursing pond
Fig 2: Percentages of different phytoplankton communities during the study period (a, b c, are indicating the size category as small, medium and large)
Fig 1 in Monitoring and assessing the physico-chemical water properties and planktonic communities in tilapia nursing pond
Fig 1: Percentages of total phytoplankton and total zooplankton in all size categorized pond during the sampling period
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
Figure 4 in Skeletochronology in long tubular bones of the Javan water monitor lizard, Varanus salvator bivittatus in the juvenile stage (Lacertilia: Varanidae)
Figure 4. Cross sections of the diaphysis fibula bone in four individuals with SVLs between 14.4 cm to 25.0 cm. Dotted arrow: endosteal bone; black solid arrow: LAG; red solid arrow: resorption line (RL); yellow solid arrow: additional resting Line (AdL). The scale bar is equal to 200 m.
Figure 1 in Skeletochronology in long tubular bones of the Javan water monitor lizard, Varanus salvator bivittatus in the juvenile stage (Lacertilia: Varanidae)
Figure 1. Measurement of bone cross section. d: the longest part of the marrow cavity; D: the longest part of the bone diameter; MP: the widest point of bone thickness.
Figure 3 in Skeletochronology in long tubular bones of the Javan water monitor lizard, Varanus salvator bivittatus in the juvenile stage (Lacertilia: Varanidae)
Figure 3. Cross section of the seven long tubular bones of a juvenile female Varanus salvator bivittatus with one LAG (SVL: 22.2 cm). MC: marrow cavity; dotted arrow: endosteal bone; red solid arrow: LAG.
Figure 2 in Skeletochronology in long tubular bones of the Javan water monitor lizard, Varanus salvator bivittatus in the juvenile stage (Lacertilia: Varanidae)
Figure 2. Cross section of the seven long tubular bones of juvenile male Varanus salvator bivittatus with zero LAG (SVL: 14.4 cm). MC: marrow cavity; dotted arrow: endosteal bone.
Wind turbine condition monitoring dataset of Fraunhofer LBF
<h3>Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring (CM) applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality.</h3> <h3>See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5</h3>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.