Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,849
datasets available to search
ShareScore release 0.9.0
Dataset results
7,849 results for “Conservancy”
FIGURE 1 in Enduring the Earthquake: Rediscovery of the Critically Endangered Mesilau Stream Toad, Ansonia guibei Inger, 1966 (Anura, Bufonidae) and its conservation implications
FIGURE 1. Ansonia guibei and its habitat at Mesilau, Sabah. A. Mesilau Cave, the type locality of A. guibei. B. Dorsolateral aspect of A. guibei (BORNEENSIS HEP 10605) in life. C. Venter of A. guibei (BORNEENSIS HEP 10605) in life. D. Dorsum of A. guibei (BORNEENSIS HEP 10605) in preservation. E. Venter of A. guibei (BORNEENSIS HEP 10605) in preservation. Photographs by Evan S.H. Quah.
Data from: Evaluating diversionary feeding as a method to resolve conservation conflicts in a recovering ecosystem.
<p>Contains R-project code, and dataset for Multinomial analysis presented in the paper: Evaluating diversionary feeding as a method to resolve conservation conflicts in a recovering ecosystem. Accepted for Publication in the Journal of Applied Ecology 30.04.2024. </p> <p> </p> <p> </p>
Figure 7 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 7: Research output for the Southeast Asian region by thematic area presented as decadal totals.
Figure 4 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 4: Halophila sp. 2 collected in 1997 at a mangrove area of Teluk Sepinong, Sandakan, Sabah, Malaysia. Photo credit: © Japar Sidik.
Figure 2 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 2: Marine provinces and ecoregions of Southeast Asia, based on Spalding et al. (2007). Provinces are made out of ecoregions with the following codes: 20108 Northern Bay of Bengal; 20109 Andaman and Nicobar Islands; 20110 Andaman Sea Coral Coast; 20111 Western Sumatra; 20112 Gulf of Tonkin; 20114 South China Sea Oceanic Islands; 20115 Gulf of Thailand; 20116 Southern Viet Nam; 20117 Sunda Shelf/Java Sea; 20118 Malacca Strait; 20119 Southern Java; 20120 Cocos-Keeling/Christmas Island; 20126 Palawan/North Borneo; 20128 Sulawesi Sea/Makassar Strait; 20129 Halmahera; 20130 Papua; 20131 Banda Sea; 20132 Lesser Sunda; 20133 Northeast Sulawesi; 20139 Arafura Sea.
Figure 3 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 3: Halophila major from Tanjung Adang Laut, Sungai Pulai estuary, Johor (refer also to Nguyen et al. 2014 for morphological and genetic identification of the species). Photo credit: © Muta Harah.
Figure 8 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 8: Roadmap for addressing conservation challenges facing seagrass in the Southeast Asian region. Challenges can have multiple solutions and these solutions can contribute to a final aim. The challenges are listed at the bottom of the figure in red ovals and the final aims are at the top of the figure in blue boxes. Intermediate solutions are presented in the middle in green boxes. Dotted lines represent linkages betweeen the challenges and the intermediate solutions, with each dotted line colour coded specific to a challenge. Intermediate solutions that contribute to a final aim are joined by dashed and solid lines and colour coded.
Figure 1 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 1: Total population by country in Southeast Asia. Population figures derived from www.worldatlas.com.
Figure 6 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 6: Research output per decade by country/territory. Numbers based on searches on Web of Science and updated from Ooi et al. (2011a).
Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024
<h2>Description</h2> <p>This repository contains gunshot sound training data, testing data, and the Convolutional Neural Network (CNN) model used in our study on gunhunting patterns in Gabon. The dataset includes hundreds of audio recordings containing gunshots and other environmental sounds, their associated annotations, and the trained Koogu machine-learning model.</p> <h2>Data Summary</h2> <ul> <li><strong>Data Types:</strong> Gunshot sound recordings, non-gunshot sound recordings, annotations, model files. Data are in the format required for Koogu, where training and test data are contained in folders of audio files with associated folders containing the Raven Pro selection tables. </li> <li><strong>Data Format:</strong> <ul> <li>Audio files: .wav</li> <li>Annotations: .txt</li> <li>Model: TensorFlow/Keras model (.h5)</li> <li>Code: .py</li> </ul> </li> </ul> <h2>Data Details</h2> <p><em>Training and Test Data:</em></p> <ul> <li>train_annotations <ul> <li>"Training_Gunshots_Batch1.txt" is a Raven Pro selection table that contains 203 gunshot annotations that were detected through the spectral cross-correlation analysis. </li> <li>"Training_Gunshots_Batch2.txt" is a Raven Pro selection table that contains 223 gunshot annotations that were detected through the initial Koogu model that was run over the whole dataset.</li> <li>"Training_NotGunshots.txt" is a Raven Pro selection table that contains 8,614 non-gunshot annotations, including sounds from branch snaps, tree falls, calls of the putty-nosed monkey, ambient noise, etc.</li> </ul> </li> <li>train_audio <ul> <li>"audio_files" contains 184 audio files associated with the selection table "Training_Gunshots_Batch1.txt"</li> <li>"audio_files_second_batch" contains 203 clips associated with the selection table "Training_Gunshots_Batch2.txt". </li> <li>"other_clips" contains 9,274 clips associated with the selection table "Training_NotGunshots.txt".</li> </ul> </li> <li>test_annotations <ul> <li>"allgunshots_test_gunshots_adjusted_final_clean_20221120.txt" is a Raven Pro selection table that contains the 138 gunshot annotations associated with the files in the "test_audio" folder. This comprises the test dataset. </li> </ul> </li> <li>test_audio<br> <ul> <li>Contains the 131 recordings associated with the 138 gunshot annotations in "allgunshots_test_gunshots_adjusted_final_clean_20221120.txt"</li> </ul> </li> </ul> <p><em>Koogu Model:</em></p> <ul> <li>qDN_4x8_x4_16_Allsites_V3<br> <ul> <li>This is the Koogu model used in this study. It can be run using the script "Koogu_Run_Local_Python_Gabon_ModelV3.py" that you modify depending on the locations of your data and model. </li> </ul> </li> </ul> <p><em>CoLab</em>: An example Koogu CoLab page that contains the code used to train and test the model is available <a href="https://colab.research.google.com/drive/1TerOhzsCs9zSMj9uQ31HX7XPKG1XFYCt?usp=sharing">here.</a></p> <h2>Data Collection</h2> <ul> <li><strong>Collection Method:</strong> BAR-LT audio recorders deployed in 110 sites within Gabonese national parks, logging concessions, and community forests.</li> <li><strong>Time Period:</strong> Recordings from February 2021 to June 2022.</li> <li><strong>Geographical Information:</strong> Specific coordinates provided upon request. </li> </ul> <h2>Data Preparation</h2> <ul> <li><strong>Downsampling:</strong> Recordings were downsampled from 44.1 kHz to 4 kHz.</li> <li><strong>Segmentation:</strong> Audio recordings split into consecutive 2.25-second segments with 1.5 seconds of overlap.</li> <li><strong>Normalization:</strong> Waveform normalized to [-1.0, 1.0].</li> <li><strong>Spectrogram Computation:</strong> Using a 64 ms window length and 50% overlap, trimmed to 10–1200 Hz frequency range.</li> <li><strong>Training Inputs:</strong> Consisted of 584 positive class (gunshots) and 25842 negative class spectrograms.</li> </ul> <h2>Requirements</h2> <ul> <li><strong>Software Requirements:</strong> Python 3.8, TensorFlow 2.5, NumPy, Pandas.</li> <li><strong>Hardware Requirements:</strong> GPU with at least 8GB VRAM recommended.</li> </ul> <h2>References</h2> <ul> <li>For more information about this dataset and the model specifications, please consult "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" (Yoh et al. 2024)</li> </ul> <h2>How to Cite this Dataset:</h2> <ul> <li>If you use this dataset in your work, please cite it: <ul> <li>Gottesman, B. (2024). Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024 (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11192704" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11192704</a></li> </ul> </li> </ul>
Successful conservation translocation: Population dynamics of tiger recovery in Panna Tiger Reserve, Central India
<p>Tiger (<em>Panthera tigris</em>) is an indicator species of ecological health and conservation efforts. Due to excessive poaching, the tiger was locally extinct in Panna Tiger Reserve, central India. Subsequent successful reintroduction efforts have brought the species back from the verge of extinction and have demonstrated the success of conservation translocations in response to such critical situations.</p> <p>To understand the demographic characteristics of the tigers reintroduced to Panna Tiger Reserve, we used an ensemble approach of different sampling techniques and direct observations from a long-term data-set spanning more than 10 years. We evaluated different demographic indicators (population status, growth rate, mean litter size, inter-birth interval, and survival probability).</p> <p>Since reintroduction in 2009, 18 females have recruited 120 cubs from 45 litters. This led to 59 individuals in 2021 with a growth rate of ~26%. The mean litter size was 2.66 (SE 0.1), and the inter-birth interval was 19.16 months (SE 0.5). The high survival rate of the reintroduced population (0.82±0.2) helped to achieve the success of reintroduction. We observed non-constant mortality trajectories for both sexes (higher survival probabilities for females) with a moderately higher risk of death in younger (<1 year) and older (>10 years) individuals.</p> <p>Our results showed the effectiveness of translocation and conservation efforts. The recovered population can be used as a founder for augmentation in other recovering tiger populations. A long-term tiger-centric management plan should be implemented in the area adjacent to Panna Tiger Reserve to conserve and secure the habitat of the entire landscape for the long-term survival of the reintroduced population in a metapopulation framework.</p>
Promoting conservation agriculture through push-pull technology as an agroecological transition.
<p>Abstract <br>Low soil fertility, weeds, pests, and climatic change severely threaten crop productivity and agricultural <br>sustainability, especially in SSA. Despite decades of research finding adequate technical solutions for most <br>situations of food systems, the problem of low food productivity has persisted. In an effort to counter this, <br>intensive agricultural systems have been mooted including the high application of agrochemicals to control <br>weeds, and pests and increase production. However, these initiatives have not lasted beyond the project cycle <br>and they have instigated land degradation through unsustainable practices. As a solution, conservation <br>agricultural practices have been promoted among small-scale farmers. These practices focus on minimizing soil <br>disturbance, crop diversification, and cover cropping. Push-pull technology is an aspect of conservation <br>agriculture where intercropping a cereal crop with a repellent plant, such as desmodium and planting an <br>attractive trap plant, such as brachiaria or Napier grass as a border crop around this intercrop. This paper aimed <br>at reviewing existing literature to establish the linkage between conservation agriculture components, push-pull <br>technology, and a sustainable agroecological transition. A list of questions directed the discussion where push<br>pull technology has been proven to be an aspect that promotes conservation agriculture. It has been able to <br>increase crop yields, reduced tillage, established a cover crop on the farm, and further, PPT has a regenerative <br>aspect through the integration of livestock husbandry providing organic manure that and together with the <br>nitrogen fixation ability of the grass, improve soil fertility, conserved soil moisture, and reduce erosion. This <br>reduces the use of inorganic input, and machinery making farming economical for small-holder farmers. </p>
Figure 6. Burned area per month detected between 1985 and 2020 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 6. Burned area per month detected between 1985 and 2020 in São João del-Rei, south-eastern Brazil. Data from the MapBiomas (2022). The dotted line indicates the average across months.
Figure 5 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 5. Arrangement diagram of the Ochre-breasted pipit nesting sites and unused sites in a grassland habitat in São João del-Rei, south-eastern Brazil. The first two components of a principal component analysis are shown: square = nesting sites located in small patches of grassland vegetation with more than 2 years of post-burn regeneration; triangle = nesting sites located in vegetation with> 1 year <2 years of regeneration; and cross = unused sites. PC = principal component. Attribute legend: liveveg = covered with living vegetation; deadveg = covered with dead vegetation; bareground = exposed soil; dens10 = vegetation density between 0 and 10 cm in height; dens = average vegetation density; height = height below which 90% of the vegetation density is located (cm).
Figure 4. Ochre-breasted pipit nestlings with about 6 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 4. Ochre-breasted pipit nestlings with about 6 (a) and 11 (b) days of life, as photographed in nest #3. Nest found in a grassland in São João del-Rei, south-eastern Brazil, in October 2016. Photos: VTL.
Figure 3 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 3. Variation in the shape and colour of eggs of the Ochre-breasted pipit. Nests found in a grassland habitat of São João del-Rei, south-eastern Brazil, between October and November 2016: (a) one of the eggs of nest #2; (b) the two-egg clutch of nest #4; (c) the three-egg clutch of nest #3, showing a more round-shaped egg on the left. The white bar is 10 mm in scale. Photos: VTL.
Figure 2 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 2. Nest #4 illustrating the two poorly defined layers of the structural zone of the Ochrebreasted pipit nests found in São João del-Rei, south-eastern Brazil, between October and November 2016: (a) outermost layer composed of small basal meristems of tussock grasses and/or by (b) thin grass stems with their leaves still attached. This outermost layer envelops the innermost layer (c), which is built with narrow filiform grass leaves tightly intertwined. Photos: VTL.
Data from: People's appreciation of colorful field margins in intensively used arable landscapes and the conservation of plants and invertebrates
<p>Sown field margins can improve the conservation of biodiversity in rural areas and can contribute to the aesthetics of rural landscapes, thereby potentially increasing public support for agri-environmental measures. However, these two functions do not necessarily coincide. This raises the question whether field margins that are appreciated for their contribution to landscape aesthetics also deliver on the conservation of biodiversity. We conducted choice experiments with different groups of citizens and collected biodiversity data in the Netherlands, to investigate if the number of colors and vegetation cover in field margins increased respondents' appreciation for them, and how these visual cues correlated with taxonomic diversity and abundance of plants and invertebrates in those field margins. Using manipulated photos, we also assessed whether the presence of colorful field margins in a range of different rural landscapes increased respondents' appreciation of those landscapes. Respondents preferred colorful margins with high vegetation cover and showed a preference for green rural landscapes with colorful field margins. The presence of colorful field margins increased landscape aesthetics most in the least appreciated landscapes. The number of colors correlated positively with the diversity of sown and spontaneous plant species, and overall invertebrate abundance and abundance of predatory invertebrates, but was not related to invertebrate diversity. Our results show for the first time that colorful field margins support both public appreciation and diversity of plants and abundance of ground-dwelling invertebrates, with potential advantages to farmers in terms of natural pest control, at least in intensively used agricultural landscapes. However, management practices to maintain a high number of colors over time may be detrimental for invertebrate diversity. To optimize the different functions, we recommend that field margin layouts should consist of a perennial part that is allowed to develop over time, in combination with a part that is managed for its colorfulness.</p>
Fig. 5 in Inventaire et conservation des abeilles sauvages (Hymenoptera:
Fig. 5. Répartition des espÈces menacées parmi les différents types d'habitats: (a) recouvrement des espÈces menacées parmi les cinq habitats (b) répartition des espèces en fonction de leur statut IUCN.
Fig. 4 in Inventaire et conservation des abeilles sauvages (Hymenoptera:
Fig. 4. Densité d'observation des espÈces menacées (statut UICN national CR, EN ou VU): le gradient de couleurs représente le nombre d'espèces menacées observées dans des mailles de 5x5 km. Fond de carte: © OpenStreetMap contributors.
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.