Find research datasets worth reusing

opencc-by-4.0Sep 2019View details →

Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.

<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>

opencc-by-4.0Dec 2016View details →

Figure 1 in Phylogenetic relationships of the vulnerable wild cattle, Malayan gaur (Bos gaurus hubbacki), and its hybrid, the selembu, based on maternal markers

Figure 1. Neighbor-joining tree of the 12S rRNA gene. The numbers at the branches stand for bootstrap values (%) of 1000 replications.

Fig. 7 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 7. Schematic of life cycle of Northern Pine Snakes, indicating periods of high vulnerability to human disturbances, such as fire, off-road vehicles, hunting, and poaching.

Fig. 5 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 5. Activity of snakes in Fall 2017 and Fall 2018 as a function of the maximum daytime temperature and the previous night's low temperature.

Fig. 3 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 3. All activity of snakes at four dens in Bass River State Forest in 2018 as a function of date and soil surface temperature.

Fig. 2 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 2. All activity of snakes at den 1 (Bass River State Forest) in 2017 as a function of date and soil surface temperature. The colored markers indicated by 9-digit numbers in the legend represent individually tagged snakes. Data for snakes during the period from 29 October to 11 November (red line) were not recorded because the maximum number of data points the receiver could store was reached.

Fig. 4 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 4. Fall activity of snakes in Fall 2017 and Fall 2018 as a function of time of day and surface soil temperature. Activity type is noted by each symbol.

Fig. 6 in Vulnerability of Northern Pine Snakes (Pituophis melanoleucus Daudin, 1803) during fall den ingress in New Jersey, USA

Fig. 6. Activity of two hatchlings (tag numbers 845090639 and 845090603) in the fall of 2018 at den (Davenport) as a function of date and soil surface temperature.

opencc-by-4.0Sep 2024View details →

Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning (Dataset)

<div> <h2>Overview</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#overview"></a></div> <p>This repository contains the code and resources for the project titled <strong>"Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning"</strong>. The goal of this project is to identify regions where individuals are living under precarious conditions and facing neglected basic needs, a situation often seen in Brazil. This concept is referred to as "human vulnerability" and is exemplified by families living in inadequate shelters or on the streets in both urban and rural areas.</p> <p>Focusing on the Federal District of Brazil as the research area, this project aims to develop two novel public datasets consisting of satellite images. The datasets contain imagery captured at 50m and 100m scales, covering regions of human vulnerability, traditional areas, and improperly disposed waste sites.</p> <p>The project also leverages these datasets for training deep learning models, including <strong>YOLOv7</strong> and other state-of-the-art models, to perform image segmentation. A comparative analysis is conducted between the models using two training strategies: training from scratch with random weight initialization and fine-tuning using pre-trained weights through <strong>transfer learning</strong>.</p> <div> <h3>Key Achievements</h3> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#key-achievements"></a></div> <ul> <li>Two new satellite image datasets focusing on human vulnerability and improperly disposed waste sites, available in public domains.</li> <li>Comparison of image segmentation models, including <strong>YOLOv7</strong> and <strong>Segmentation Models</strong>, with performance metrics.</li> <li>Best F1-scores: 0.55 for <strong>YOLOv7</strong> and 0.64 for <strong>Segmentation Models</strong>.</li> </ul> <p>This repository provides the code, models, and data pipelines used for training, evaluation, and performance comparison of these deep learning models.<br><br></p> <div> <h2>Citation (Bibtex)</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#citation-bibtex"></a></div> <pre><code>@TECHREPORT {TechReport-Julia-Laura-HumanVulnerability-2024, author = "Julia Passos Pontes, Laura Maciel Neves Franco, Flavio De Barros Vidal", title = "Detecção de Áreas com Atividades de Vulnerabilidade Humana utilizando Imagens Públicas de Satélites e Aprendizagem Profunda", institution = "University of Brasilia", year = "2024", type = "Undergraduate Thesis", address = "Computer Science Department - University of Brasilia - Asa Norte - Brasilia - DF, Brazil", month = "aug", note = "People living in precarious conditions and with their basic needs neglected is an unfortunate reality in Brazil. This scenario will be approached in this work according to the concept of \"human vulnerability\" and can be exemplified through families who live in inadequate shelters, without basic structures and on the streets of urban or rural centers. Therefore, assuming the Federal District as the research scope, this project proposes to develop two new databases to be made available publicly, considering the map scales of 50m and 100m, and composed by satellite images of human vulnerability areas, regions treated as traditional and waste disposed inadequately. Furthermore, using these image bases, trainings were done with the YOLOv7 model and other deep learning models for image segmentation. By adopting an exploratory approach, this work compares the results of different image segmentation models and training strategies, using random weight initialization (from scratch) and pre-trained weights (transfer learning). Thus, the present work was able to reach maximum F1 score values of 0.55 for YOLOv7 and 0.64 for other segmentation models." } </code></pre> <div> </div> <div> <h2>License</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#license"></a></div> <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>

opencc-zeroJan 2024View details →

Linked collectors and determiners for: A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov..

Natural history specimen data linked to collectors and determiners held within, "A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Oct 2024View details →

Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites

<p>This data comprises all input and output, including intermediate results for the evaluation of the tool cpg-contract-checker(CCC) and the crawled data and results for the study published under the name "Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites".<br>We conducted a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provided tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. The paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. The tools are used to realize a study pipeline for which the dataset and (intermediate) results are contained in this archive.</p>

opencc-by-4.0Jun 2012View details →

Figure 1 in Belosynapsis vivipara (Dalzell) C.E.C. Fisch. (Commelinaceae), a vulnerable spiderwort, rediscovered after sixteen decades from Maharashtra, India

Figure 1. Distribution map of Belosynapsis vivipara showing the type locality (Parva Ghat) and Chandoli National Park.

dryad40/100

Climate change exposure and vulnerability of the global protected area estate from an international perspective

<p>Aim: Protected areas are essential to conserve biodiversity and ecosystem benefits to society under increasing human pressures of the Anthropocene. Anthropogenic climate change, however, threatens the enduring effectiveness of protected areas in conserving biodiversity and providing ecosystem services, because it modifies and redistributes biodiversity with unknown consequences for ecosystem functioning within protected areas. Here we assess (1) the climate change exposure of the global terrestrial protected area estate and (2) the climate change vulnerability of national protected area estates.</p> <p>Location: Terrestrial protected areas worldwide.</p> <p>Methods: We calculated local climate change exposure as predicted climate anomalies between the present and 2070 using ten global climate models, two emission scenarios (RCP 4.5 and 8.5) and the finest spatial resolution available for global climate projections (approx. 1 km). We estimated the climate change vulnerability of national protected area estates by analysing countrywide relationships between protected areas' climate anomalies and other protected area characteristics, i.e. area, elevation, terrain ruggedness, human footprint and irreplaceability for globally threatened species.</p> <p>Results: We found predicted climate anomalies highest in protected areas of (sub-)tropical countries. The correlations between climate anomalies and protected area characteristics strongly differ between countries. Globally, protected areas showing large climate anomalies tend to be at high elevation and highly irreplaceable for threatened species, increasing climate change vulnerability. These protected areas are relatively large in area, of high topographic heterogeneity and less pressured by humans, decreasing climate change vulnerability.</p> <p>Main conclusion: This study reveals potential hotspots of climate change impact inside the terrestrial protected area estate. It thus supports and guides climate-smart conservation policy and management, particularly national to local authorities, to ensure the future effectiveness of protected areas in preserving biodiversity and ecosystem benefits under climate change.</p>

opencc-zeroAug 2021View details →

Fig. 4 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species

Fig. 4. Multidimensional Scaling (MDS) plot (stress: 0.0045) performed using average pairwise TN93 (Tamura & Nei, 1993) distances among investigated Lutrogale perspicillata groups created according to the country of origin of samples (modern + museum DNA and GenBank entries).

Fig. 3. A in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species

Fig. 3. A, Lutrogale perspicillata network computed using haplotypes (h) from the 305 bp-long sequence alignment (modern + museum DNA and GenBank entries). A scale to infer the number of sequences for each pie (i.e., haplotype) was provided together with a length bar to compute the number of mutational changes. The colour of each country and the number of each haplotype are indicated. See Table S1 for more details. B, Mismatch Distributions (MD) of the mtDNA pairwise differences (dotted: observed; line: expected) calculated for South East Asia haplogroup (Fig. 3A). Estimates of FS and R2 statistics (with related P values), r (raggedness index) and the outcome of SSD and SSD* test under a model (H0) of sudden demographic and spatial population expansion, respectively, are provided.

Fig. 2 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species

Fig. 2. Photos of MNHN-ZM-MO-2001-350, L. p. perspicillata holotype resident in the mammal collection of the National Museum of Natural History of Paris, France. A, right side, lateral view (bar length = 20 cm); B, left forelimb, lateral view; C, basement, in French "Lutra perspicillata = Lutra leptonix Horsf., loutre de Java par m Diard, mai 1821, la tête est au lab d'anatomie", which can be translated into and interpreted as: "Lutra perspicillata = Lutra leptonix (Horsfield, 1824), Java otter from M. Diard, May 1821, skull is in the lab of anatomy" (see also Material and Methods). Photos courtesy and copyright: © MNHN - RECOLNAT - Laura Flamme - 2014.

Fig. 1 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species

Fig. 1. Lutrogale perspicillata distribution (in yellow; see insets for Iraq and Pakistan) including sampling localities of modern (white circles) and museum (green squares) individuals. As far as the latter are concerned, we reported only sites for which samples were successfully investigated (see Table S1 for the entire sample size of this study; symbol "?" stands for unknown locality). The white stars indicate, in Iraq, the locality (TaqTaq, Kurdistan) where the sample of Omer et al. (2012) was collected, in Cambodia/Thailand and Malaysia, the country/ies of origin of EF472348 and KY117557 GenBank sequence, respectively. In Iraq, Pakistan, and supposedly Java, Indonesia, the green squares indicate localities (when known) of L. p. maxwelli, L. p. sindica, and L. p. perspicillata museum holotypes, respectively. Finally, Naga Hills at the border between Myanmar and India as well as Bahoo-Kalat River Basin between Iran and Pakistan are indicated (see text for more details). The species' geographic range was adapted from IUCN (International Union for Conservation of Nature) 2015. Lutrogale perspicillata. The IUCN Red List of Threatened Species 2019-3 was modified using CorelDraw!12 (2003). Digital images (insets) were obtained from Google Earth 7.1.5.1557 (2015 Google Inc.) and Google Earth map data (Data SIO, NOAA, U.S. Navy, NGA, GEBCO - Image Landsat). Please note that thick dotted lines mark out new borders for L. p. sindica and L. p. perspicillata subspecies as established in this study (see text for more details).

opencc-by-4.0Sep 2021View details →

Data from: Detecting the effects of rapid tectonically-induced subsidence on Mayotte Island since 2018 on beach and reef morphology, and implications for coastal vulnerability to marine flooding

<p>This dataset contains data from the monitoring morphological evolution of beaches and coral reefs in Mayotte island.  Mayotte, part of the coral reef-fringed Comoro archipelago in the SW Indian Ocean, experienced in 2018 and 2019 an intense seismic crisis. The repeated earthquake activity since May 2018 has been associated with deformation of the surface of Mayotte, resulting in land subsidence.</p> <p>The earlier 2006-2008 profiles were realized using a Leica TC 407® total station, and referenced to local IGN 50 benchmarks. The more recent 2019, 2020, and 2021 surveys were carried out using a GNSS differential Trimble R8S® system. Given the rapid subsidence that has affected Mayotte, the benchmarks used in this study, like others in Mayotte, need to be recalibrated by the IGN (French Institut Géographique National) and SHOM. This has still not yet been done, as the final outcome of the vertical island movements is still not clear.</p>

opencc-by-nc-nd-4.0Sep 2021View details →

Social vulnerability to flooding in Ecuador : input variables, PCA vs Expert composite indices

<p><strong>Social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. This research specifically supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action protocol.</strong></p> <p>The dataset presents the results from the analysis of the social vulnerability to flooding in Ecuador, from individual input variables to the composite indices outputs. The results are available at the Parroquia level in Ecuador (admin level 3), for 1032 Parroquia excluding the Galapagos Islands.</p> <ul> <li>The dataset comprises, for each Parroquia, the estimation of <strong>15 variables characterizing the social vulnerability to flooding specific to Ecuador context</strong>. The variables are selected from literature review and consultation with Ecuadorian Red Cross disaster practitioners : <em>Disability, Poverty incidence, Gini Index, Agricultural labor share, Vectorborne disease incidence, Waterborne disease incidence, Social Security affiliation, Education level, Sanitation, Driking water access, Power access, Road travel time, Wall structure, Mobile access and Internet access.</em> All variables are normalized from 0 to 1, directed toward increasing vulnerability, and renamed accordingly.</li> <li>In addition, the <strong>Administrative level names, PCODE, calculated Area, population density,</strong> as well as related <strong>sub-regions</strong> are also referenced.</li> <li>Individual variables are integrated into <strong>composite vulnerability indices</strong>, using two different approaches:  i) the Principal Component Analysis approach, using the first component <strong>PCA(n=1) </strong>and the first 5 components <strong>PCA(n=5)</strong> separately ; ii) the <strong>expert judgement weighting</strong> of the variables. The output composite indices, normalized from 0 to 1 are presented in 3 separated columns.</li> </ul> <p> </p>