Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Traffic and Log Data Captured During a Cyber Defense Exercise

This dataset was acquired during Cyber Czech – a hands-on cyber defense exercise (Red Team/Blue Team) held in March 2019 at Masaryk University, Brno, Czech Republic. Network traffic flows and a high variety of event logs were captured in an exercise network deployed in the KYPO Cyber Range Platform. Contents The dataset covers two distinct time intervals, which correspond to the official schedule of the exercise. The timestamps provided below are in the ISO 8601 date format.  <ul> <li>Day 1, March 19, 2019  <ul> <li>Start: 2019-03-19T11:00:00.000000+01:00 </li> <li>End: 2019-03-19T18:00:00.000000+01:00 </li> </ul> </li> <li>Day 2, March 20, 2019  <ul> <li>Start: 2019-03-20T08:00:00.000000+01:00 </li> <li>End: 2019-03-20T15:30:00.000000+01:00 </li> </ul> </li> </ul> The captured and collected data were normalized into three distinct event types and they are stored as structured JSON. The data are sorted by a timestamp, which represents the time they were observed. Each event type includes a raw payload ready for further processing and analysis. The description of the respective event types and the corresponding data files follows.   <ul> <li>cz.muni.csirt.IpfixEntry.tgz – an archive of IPFIX traffic flows enriched with an additional payload of parsed application protocols in raw JSON. </li> <li>cz.muni.csirt.SyslogEntry.tgz – an archive of Linux Syslog entries with the payload of corresponding text-based log messages. </li> <li>cz.muni.csirt.WinlogEntry.tgz – an archive of Windows Event Log entries with the payload of original events in raw XML. </li> </ul> Each archive listed above includes a directory of the same name with the following four files, ready to be processed.  <ul> <li>data.json.gz – the actual data entries in a single gzipped JSON file. </li> <li>dictionary.yml – data dictionary for the entries. </li> <li>schema.ddl – data schema for Apache Spark analytics engine. </li> <li>schema.jsch – JSON schema for the entries. </li> </ul> Finally, the exercise network topology is described in a machine-readable NetJSON format and it is a part of a set of auxiliary files archive – auxiliary-material.tgz – which includes the following.  <ul> <li>global-gateway-config.json – the network configuration of the global gateway in the NetJSON format. </li> <li>global-gateway-routing.json – the routing configuration of the global gateway in the NetJSON format. </li> <li>redteam-attack-schedule.{csv,odt} – the schedule of the Red Team attacks in CSV and ODT format. Source for Table 2. </li> <li>redteam-reserved-ip-ranges.{csv,odt} – the list of IP segments reserved for the Red Team in CSV and ODT format. Source for Table 1.  </li> <li>topology.{json,pdf,png} – the topology of the complete Cyber Czech exercise network in the NetJSON, PDF and PNG format. </li> <li>topology-small.{pdf,png} – simplified topology in the PDF and PNG format. Source for Figure 1. </li> </ul>

opencc-zeroDec 2013View details →

Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey

<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>

opencc-zeroDec 2023View details →

Marmot capture history data and growing season length data

Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots (Marmota flaviventer) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics. We were able to impute body mass for each individual twice during each year following their first capture using a similar approach to Ozgul et al. (2010) (for more details on the modeling procedure see SI Appendix within the main paper). Body mass measurements were log-transformed.

opencc-zeroApr 2024View details →

Capturing local nuisance flooding events with HOBO pendant G data loggers in Key West, Florida US

Flooding impacts social, economic, and landscape systems globally. Changing climate and growing coastal populations exacerbate the outcomes of environmental hazards. Due to the spatial variability in exposure and vulnerability, coastal flooding must be understood at high spatial and temporal resolutions. This paper presents a novel deployment technique using inexpensive accelerometers to measure local floods. The technique is tested in Key West, FL, USA using storm drains to deploy HOBO pendant G data loggers. The feasibility of the method is tested by a team of local stakeholders and researchers through four deployments between July 2019 – November 2021. All deployments resulted in 22 sensors successfully recording data, with 15 of these sensors recording flooding. Sensors captured an average of 13.58 inundation events causing the storm drains to be inundated on average 12.07% of the deployment time. Measured inundation events coincide with local National Oceanic and Atmospheric Administration (NOAA) water level measurements of high tides, which shows that high-tide inundation is captured by the accelerometers. Accelerometers are easy to deploy and accurately capture the duration of local flooding. Access to an effective and inexpensive sensor for measuring flood events can increase opportunities to measure local-scale hazards and collect important information with participation by interested parties (e.g., local governments, homeowners, schools etc.). The ease of use and successful recording of loggers can give communities access to flooding data, and in turn, increase their capacity to make data-informed decisions surrounding sea level rise adaptation.

opencc-by-4.0Nov 2024View details →

Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"

<h1>Changes applied to V2</h1> In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below. The S2RCI and MD datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature:   <ul> <li>AlphaFold2 Models: Both original and truncated structures.  </li> <li>WEBnma Modes: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models.  </li> <li>Root-Mean-Square-Fluctuations (RMSF): Profiles calculated before and after truncation of AF2 models.  </li> <li>NMR Data: Normal Mode Analysis (NMA): Performed on corresponding NMR ensembles (see below).  </li> </ul>   The NMR Data of NMA in these datasets includes:   <ul> <li>NMR ensembles  </li> <li>Individual NMR models extracted from each ensemble  </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> For detailed information, please refer to the `Readme.txt` file within each corresponding folder.   The S2 dataset includes all the features listed above, except for the NMR analysis.

opencc-by-4.0Aug 2021View details →

Data set for paper on Australian fur seal prey capture and foraging efficiency

Data set for paper on Australian fur seal prey capture and foraging efficiency

opencc-by-4.0Dec 2021View details →

evaluation data for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"

Data contains sound files of mouse vocalization needed to reproduce the evaluation results for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)" If you use any of this data, cite the original source: <a href="https://doi.org/10.1016/j.anbehav.2020.09.006">https://doi.org/10.1016/j.anbehav.2020.09.006</a>

opencc-zeroJun 2022View details →

Morphological and DNA sequence data generated by Sanger sequencing and target capture methods for moss plants in the genus Fissidens from herbarium specimens

Morphological evolution in mosses has long been hypothesized to accompany shifts in microhabitats and can be tested using comparative phylogenetics. These lines of inquiry have developed substantially, in part, by target capture sequencing allowing for phylogenomic scale data generated from herbarium specimens. In the present study, we test the relationship between taxonomically important morphological characters in the moss genus Fissidens, using both a 400-locus dataset generated using a target-capture approach as well as a three-locus phylogeny generated using sanger sequencing. Phylogenetic trees were generated using ASTRAL and Bayesian Inference and used to test the monophyly of subgenera/sections and provided the basis for ancestral character reconstruction and phylogenetic correlation analyses among five morphological characters as well as habitat moisture scored from literature. The characters axillary hyaline nodules, limbidium, costa, and peristome morphology as well as sexual system, minimum habitat moisture, average habitat moisture, maximum habitat moisture, and habitat moisture niche breadth each exhibit statistically significant phylogenetic signal. Significant correlations were found between the limbidium (phyllid/leaf border) and habitat moisture niche breadth, which could be interpreted as a more extensive limbidium enabling species to survive across a wider variety of habitats. Correlations were also found between costa anatomy and the limbidum of the gametophyte and sporophyte peristome morphology, as well as average habitat moisture and sexual system. Continued exploration of the relationships between morphological evolution, life history, and habitat will enable us to expand our understanding of functional morphology in mosses.

opencc-zeroMay 2024View details →

Data and code from: Recreational fisheries selectively capture and harvest large predators

Size and species selective harvest, inevitably alters the composition of targeted populations and communities. This can potentially harm fish stocks, ecosystem functionality, and related services, as evidenced in numerous commercial fisheries. The high popularity of rod-and-reel recreational fishing, practiced by hundreds of millions globally, raises concerns about similar deteriorating effects. Despite its prevalence, the species and size selectivity of recreational fisheries remain largely unquantified due to a lack of combined catch data and fisheries-independent surveys. This study addresses this gap by using standardised monitoring data and over 60,000 digital angling catch reports from 62 distinct fisheries. The findings demonstrate a pronounced selectivity in recreational fisheries, targeting top-predators and large individuals. Catch-and-release practices reduced the overall harvest by 60 % but did not substantially alter this selectivity. The strong species- and size-specific selectivity mirror patterns observed in other fisheries, emphasising the importance of managing the potential adverse effects of recreational fisheries selective mortality and overfishing.

opencc-zeroMay 2024View details →

Data from: Wildfire smoke impacts the body condition and capture rates of birds in California

Despite the increased frequency with which wildfire smoke now blankets portions of world, the effects of smoke on wildlife, and birds in particular, are largely unknown. We used two decades of banding data from the San Francisco Bay Bird Observatory to investigate how fine particulate matter (PM2.5) – a major component and indicator of wildfire smoke – influenced capture rates and body condition of 21 passerine or near-passerine bird species. Across all study species, we found a negative effect of acute PM2.5 exposure and a positive effect of chronic PM2.5 exposure on avian capture rates. Together, these findings are indicative of decreased bird activity or local site removal during acute periods of wildfire smoke, but increased activity or site colonization under chronic smoke conditions. Importantly, we also observed a negative relationship between chronic PM2.5 exposure and body mass change in individuals with multiple captures per season. Our results indicate that wildfire smoke likely influences the health and behavior of birds, ultimately contributing to a shift in activity and body condition, with differential short-term versus long-term impacts. Although more research is needed on the mechanisms driving these observed changes in bird health and behavior, as well as validation of these relationships in more areas, our results suggest that wildfire smoke is a potentially frequent large-scale environmental stressor to birds that deserves increasing attention and recognition.

opencc-zeroJun 2024View details →

Data from: Exon capture museomics deciphers the nine-banded armadillo species complex and identifies a new species endemic to the Guiana Shield

The nine-banded armadillo (Dasypus novemcinctus) is the most widespread xenarthran species across the Americas. Recent studies have suggested it is composed of four morphologically and genetically distinct lineages of uncertain taxonomic status. To address this issue, we used a museomic approach to sequence 80 complete mitogenomes and capture 997 nuclear loci for 71 Dasypus individuals sampled across the entire distribution. We carefully cleaned up potential genotyping errors and cross contaminations that could blur species boundaries by mimicking gene flow. Our results unambiguously support four distinct lineages within the D. novemcinctus complex. We found cases of mito-nuclear phylogenetic discordance but only limited contemporary gene flow confined to the margins of the lineage distributions. All available evidence including the restricted gene flow, phylogenetic reconstructions based on both mitogenomes and nuclear loci, and phylogenetic delimitation methods consistently supported the four lineages within D. novemcinctus as four distinct species. Comparable genetic differentiation values to other recognized Dasypus species further reinforced their status as valid species. Considering congruent morphological results from previous studies, we provide an integrative taxonomic view to recognise four species within the D. novemcinctus complex: D. novemcinctus, D. fenestratus, D. mexicanus, and D. guianensis sp. nov., a new species endemic of the Guiana Shield that we describe here. The two available individuals of D. mazzai and D. sabanicola were consistently nested within D. novemcinctus lineage and their status remains to be assessed. The present work offers a case study illustrating the power of museomics to reveal cryptic species diversity within a widely distributed and emblematic species of mammals.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 11. Accuracy of different method for unseen faces

Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)

In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models.

opencc-by-4.0May 2018View details →

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions

Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier

After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 5. Examples of the circular LBP (Huang et al., 2011)

One limitation of the basic LBP operator is that its small 3x3 neighborhood cannot capture dominant features with large scale structures. To deal with the texture at different scales the operator was later generalized to use neighborhoods of different sizes. A local neighborhood is defined as a set of sampling points evenly spaced on a circle which is centered at the pixel to be labeled. The sampling points that do not fall within the pixels are interpolated using bilinear interpolation, thus allowing for any radius and any number of sampling points in the neighborhood. Figure 5 shows some examples of the extended LBP operator where the notation (P, R) denotes a neighborhood of P sampling points on a circle of radius of R.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database

Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method

In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.