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9,302 results for “DETECT”
Data from: Environmental change, if unaccounted, prevents detection of cryptic evolution in a wild population
Detecting contemporary evolution requires demonstrating that genetic change has occurred. Mixed-effects models allow estimation of quantitative genetic parameters and are widely used to study evolution in wild populations. However, predictions of evolution based on these parameters frequently fail to match observations. Furthermore, such studies often lack an independent measure of evolutionary change against which to verify predictions. Here, we applied three commonly used quantitative genetic approaches to predict the evolution of size at maturity in a wild population of Trinidadian guppies. Crucially, we tested our predictions against evolutionary change observed in common garden experiments performed on samples from the same population. We show that standard quantitative genetic models underestimated or failed to detect the cryptic evolution of this trait as demonstrated by the common garden experiments. The models failed because: 1) size at maturity and fitness both decreased with increases in population density, 2) offspring experienced higher population densities than their parents, and 3) selection on size was strongest at high densities. When we accounted for environmental change, predictions better matched observations in the common garden experiments, although substantial uncertainty remained. Our results demonstrate that predictions of evolution are unreliable if environmental change is not appropriately captured in models.
Data from: Detecting the population dynamics of an autosomal sex-ratio distorter transgene in malaria vector mosquitoes
<p>1. The development of genetically modified mosquitoes and their subsequent field release offers innovative and cost-effective approaches to reduce mosquito-borne diseases, such as malaria. A sex-distorting autosomal transgene has been developed recently in G3 mosquitoes, a lab strain of the malaria vector Anopheles gambiae s.l. The transgene expresses an endonuclease called I-PpoI during spermatogenesis, which selectively cleaves the X chromosome to result in ~95% male progeny. Following the World Health Organization Guidance Framework for the testing of genetically modified mosquitoes, we assessed the dynamics of this transgene in large cages using a joint experimental-modelling approach.</p> <p>2. We performed a four-month experiment in indoor large cages to study the population genetics of the transgene. The cages were set up to mimic a simple tropical environment with a diurnal light-cycle, constant temperature, and constant humidity. We allowed the generations to overlap to engender a stable age structure in the populations. We constructed a model to mimic the experiments, and used the experimental data to infer the key model parameters.</p> <p>3. We identified two fitness costs associated to the transgene. First, transgenic adult males have reduced fertility and, second, their female progeny have reduced pupal survival rates. Our results demonstrate that the transgene is likely to disappear in less than three years under our confined conditions. Model predictions suggest this will be true over a wide range of background population sizes and transgene introduction rates.</p> <p>4. Synthesis and applications: Our semi-field indoor cage experiments are in line with WHO guidance recommendations in regards to the development and testing of self-limiting technologies. Since the transgenic strain (Ag(PMB)1) has been considered for genetic vector control of malaria, our results are fundamentally important for determining expectations on the persistence of the transgene post-release. Our results provide a demonstration of the self-limiting nature of the transgene, and indicate that longevity will be further reduced by fitness costs that were not previously identified. Finally, our study has showcased an alternative and effective method for characterising the phenotypic expression of a transgene in an insect pest population.</p>
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
<p>This dataset contains the files and annotations for <a href="https://propaganda.qcri.org/semeval2020-task11/index.html">SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles</a>. The task was composed by two subtasks: span identification (SI) and technique classification (TC). This dataset includes the following:</p> <ul> <li>The text files for training, development, and testing sets for both the SI and the TC tasks.</li> <li>The gold-standard files for the training sets, for both the SI and the TC task</li> </ul> <p>Our propaganda identification initiative remains active. We keep a <a href="https://propaganda.qcri.org/ptc/leaderboard.php">live leader-board</a> reporting the performance of models submited up to date.</p> <p><strong>Reference</strong></p> <p>Giovanni Da San Martino, Alberto Barrón-Cedeño, Henning Wachsmuth, Rostislav Petrov, and Preslav Nakov. 2020. <a href="https://propaganda.qcri.org/">Task 11: Detection of Propaganda Techniques in News Articles</a>. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020). Barcelona, Spain (2020)</p> <p> </p> <pre><code>@InProceedings{SemEval20-11-DaSanMartino, author = "Da San Martino, Giovanni and Barr\'{o}n-Cede\~no, Alberto and Wachsmuth, Henning and Petrov, Rostislav and Nakov, Preslav", title = "{SemEval}-2020 Task 11: {D}etection of Propaganda Techniques in News Articles", pages = "", abstract = "We describe the outcome of the SemEval 2020 Task 11 on the detection of propaganda in news articles. We present two tasks. In the first task, systems are asked to identify specific text spans in a free text where propaganda is being applied. In the second task, systems are asked to identify the propaganda technique being applied in a text span. We describe the construction of the evaluation framework (dataset and evaluation metrics) as well as the approaches explored by the different participants. ", crossref = "SemEval20" }</code></pre> <p> </p>
Data from: Fluid preservation causes minimal reduction of parasite detectability in fish specimens: a new approach for reconstructing parasite communities of the past?
<ol> <li>Long-term datasets are needed to evaluate temporal patterns in wildlife disease burdens, but historical data on parasite abundance are extremely rare. For more than a century, natural history collections have been accumulating fluid-preserved specimens, which should contain the parasites infecting the host at the time of its preservation. However, before this unique data source can be exploited, we must identify the artefacts that are introduced by the preservation process. Here, we experimentally address whether the preservation process alters the degree to which metazoan parasites are detectable in fluid-preserved fish specimens when using visual parasite detection techniques.</li> <li>We randomly assigned fish of three species (<i>Gadus chalcogrammus, Thaleichthys pacificus, Parophrys vetulus</i>) to two treatments. In the first treatment, fish were preserved according to the standard procedures used in ichthyological collections. Immediately after the fluid-preservation process was complete, we performed parasitological dissection on those specimens. The second treatment was a control, in which fish were dissected without being subjected to the fluid-preservation process. We compared parasite abundance between the two treatments.</li> <li>Across 298 fish individuals and 59 host–parasite pairs, we found few differences between treatments, with 24 of 27 host–parasite pairs equally abundant between the two treatments. Of these, one pair was significantly more abundant in the preservation treatment than in the control group, and two pairs were significantly less abundant in the preservation treatment than in the control group.</li> <li>Our data suggest that the fluid-preservation process does not have a substantial effect on the detectability of metazoan parasites. This study addresses only the effects of the fixation and preservation process; long-term experiments are needed to address whether parasite detectability remains unchanged in the months, years, and decades of storage following preservation. If so, ecologists will be able to reconstruct novel, long-term datasets on parasite diversity and abundance over the past century or more using fluid-preserved specimens from natural history collections.</li> </ol>
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
<p>This dataset comprises of the IDL code referenced in the 'Open Research' section of the Kaye and Pittman (2020) study 'Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system' published in <em>Methods in Ecology and Evolution</em> (<a href="https://doi.org/10.1111/2041-210X.13402">https://doi.org/10.1111/2041-210X.13402</a>).</p> <p>This study describes a proof‐of‐concept autonomous unmanned aerial vehicle (UAV) system that utilizes the fluorescence characteristics unique to different materials to scan and acquire targets in the field e.g. fossils, rocks and minerals, organisms and archaeological artefacts. This is possible because these targets are often highly fluorescent against lower fluorescence backgrounds and may exhibit different colours. Fluorescence is stimulated by a near‐UV laser that is projected across the ground as a horizontal line directly below the UAV. The IDL code is for laser line and colour extractions in the laser scan strip. The raw .jpeg data for the IDL code is not provided here as this depends on what target is being scanned. All image data are made available in the paper. Additional contextual information is provided in the '2 MATERIALS AND METHODS' section of the paper, especially in Figure 3.</p>
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
<p>As species richness varies along the tree of life, there is a great interest in identifying factors that affect the rates by which lineages speciate or go extinct. To this end, theoretical biologists have developed a suit of phylogenetic comparative methods that aim to identify where shifts in diversification rates had occurred along a phylogeny and whether they are associated with some traits. Using these methods, numerous studies have predicted that speciation and extinction rates vary across the tree of life. In this study we show that asymmetric rates of sequence evolution rates lead to systematic biases in the inferred phylogeny, which in turn lead to erroneous inferences regarding lineage diversification patterns. The results demonstrate that as the asymmetry in sequence evolution rates increases, so does the tendency to select more complicated models that include the possibility of diversification rate shifts. These results thus suggest that any inference regarding shifts in diversification pattern should be treated with great caution, at least until any biases regarding the molecular substitution rate have been ruled out.</p>
Data from: Detecting and quantifying social transmission using network-based diffusion analysis
<p>1. Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging.</p> <p>2. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities.</p> <p>3. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of: selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection.</p> <p>4. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship.</p> <p>5. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed NBDA package written in the R programming language.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Test Set)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>This is the Test Set of RibFrac dataset, including 160 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-test-images.zip: 160 chest-abdomen CTs in NII format (nii.gz) </li> </ol> <p>Note that only the images are released; the corresponding ground truth labels are not available. Check the <a href="https://ribfrac.grand-challenge.org/">MICCAI 2020 RibFrac Challenge website</a> to evaluate your algorithm.</p> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
<p>The spectra were provided as unprocessed raw data in the manufacturers data format (Bruker), as labelled two zip archives with SARS CoV 2 positives and negative, according to the reviewer's recommendation.</p> <p>This information belongs to the publication (in review in <em>Journal of Virological Methods</em>)<br> "A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs"<br> All the information belongs to the National Reference Institute, INEI-ANLIS DR CARLOS G MALBRAN, BUENOS AIRES, ARGENTINA.</p>
Data belonging to paper 'Detecting Faces, Medium Types, Gender in Historical Advertisments'
<p>This dataset contains annotations, intermediate data, and outputted models for the VISART paper <em>'Detecting Faces, Medium Types, Gender in Historical Advertisments</em>.'</p> <p> </p> <p>See the <a href="https://github.com/melvinwevers/detecting_faces-medium-types-gender">repo</a> on GitHub for info on how to use this data.</p>
Swim with the tide: tactics to maximise prey detection by a specialist predator, the greater sea snake (Hydrophis major)
<p><span><span><span><span><span><span><span><span><span><span><span>The fitness of a predator depends upon its ability to locate and capture prey; and thus, increasing dietary specialization should favor the evolution of species-specific foraging tactics tuned to <span><span>taxon</span></span>-specific habitats and cues. Within marine environments, prey detectability (e.g., via visual or chemical cues) is affected by environmental conditions (e.g., water clarity and tidal flow), such that specialist predators would be expected to synchronize their foraging activity with cyclic variation in such conditions. In the present study, we combined behavioral-ecology experiments on captive sea snakes and their prey (catfish) with acoustic tracking of free-ranging sea snakes, to explore the use of waterborne chemical cues in this predator-prey interaction. In coral-reef ecosystems of New Caledonia, the greater sea snake (<i>Hydrophis major</i>) feeds only upon striped eel catfish (<i>Plotosus lineatus</i>). Captive snakes became more active after exposure to waterborne chemical cues from catfish, whereas catfish did not avoid <span><span>chemical</span></span> cues from snakes. Movement patterns of tracked snakes showed that individuals were most active on a rapidly falling tide, which is the time when <span><span>chemical</span></span> cues from hidden catfish are <span><span>likely to be</span></span> most readily available to a foraging predator. By synchronizing foraging effort with the tidal cycle, greater sea snakes may be able to exploit the availability of chemical cues during a rapidly falling tide to maximize efficiency in locating and capturing prey. </span></span></span></span></span></span></span></span></span></span></span></p>
Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Detecting and Tracking Drift in Quantum Information Processors
<p>This is supplemental data and code for:<strong> </strong>T. Proctor et al, <a href="https://www.nature.com/articles/s41467-020-19074-4"><em>Detecting and tracking drift in quantum information processors</em></a>, Nat. Comm. 11, 5396 (2020).</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does <em>not</em> work on the latest full release of pyGSTi (0.9.9). It is anticipated that it will work with the next full release of pyGSTi (0.9.10).</p> <p>Below is a basic guide to navigating this SI:</p> <p><strong>Time-resolved Ramsey tomography on experimental data.</strong></p> <p><em>Directory: ramsey/experiment</em></p> <p>This folder contains the data and analysis code for the time-resolved Ramsey experiment, the results of which are presented in Figure 1 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis.</p> <p><strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong></p> <p><em>Directory: rb/simulation</em></p> <p>This folder contains the data and analysis code for the simulation of time-resolved RB, the results of which are presented in Figure 2 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis on the simulated data, and which can be used to run new simulations with the same noise model.</p> <p><strong>Time-resolved gate set tomography (GST) on simulated data.</strong></p> <p><em>Directory: gst/simulation</em></p> <p>This contains the data and analysis code for the simulation of time-resolved GST, the results of which are presented in Figure 2 of the paper. The raw simulated data is contained in the "data" folder. All the code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>create_simulated_data.py : this generates the simulated data. This was run using MPI on 20 cores.</li> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul> <p><strong>Time-resolved gate set tomography (GST) on experimental data.</strong></p> <p><em>Directory: gst/experiments</em></p> <p>This folder contains the data and analysis code for the two time-resolved GST experiments, the results of which are presented in Figure 3 of the paper. The raw data is contained in the two folders "data/1" and "data/2", corresponding to the first and second experiment, respectively. All analysis code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul>
Supporting data and scripts for Nature Astronomy article "Detection of two bright radio bursts from magnetar SGR 1935+2154" by Kirsten et al.
<p>This data set contains scripts and data used to analyze the observations described in Nature Astronomy article "Detection of two bright radio bursts from magnetar SGR 1935+2154" by Kirsten et al. 2020. The scripts used to create the plots therein also included.</p>
Detecting phylogenetic signal and adaptation in papionin cranial shape by decomposing variation at different spatial scales
<p>Phylogenetic reconstruction based on morphometric data is hampered by homoplasies. For example, many similarities in cranial form between primate taxa more strongly reflect ecological similarities rather than phylogenetic relatedness. However, the way in which the different cranial bones constitute cranial form is, if at all, of less functional relevance and thus largely hidden from selection. We propose that these "constructional details" are better indicators of phylogenetic history than any large-scale shape feature or raw form variable. Within a geometric morphometric context, we show how to analyze the relative extent of bones independently of differences in overall shape. We also show how to decompose total shape variation into small-scale and large-scale shape variation. We apply both methods to the midsagittal cranial morphology of papionin monkeys, which are well known for the discrepancy between morphological similarities and phylogenetic relationships. We study phylogenetic signal and functional adaptation using a molecular phylogeny and contextual data on feeding ecology and locomotor behavior. As expected, total cranial shape, bone outline shape, and large-scale shape features were only weakly associated with phylogenetic distance. But the relative bone contributions and small-scale shape features were both highly correlated with phylogenetic distances. By contrast, the association with ecological and behavioral variables was strongest for the outline shape and large-scale shape features. Studies of morphological adaptation and phylogenetic history thus profit from a decomposition of shape variation into different spatial scales.</p>
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</li> </ul> <p> </p> <p><strong>3). project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Weakly-Supervised Crack Detection Dataset
<p>This repo contains two files: crack detection dataset (weakly_sup_crackdet_dataset.zip), and pretrained TensorFlow model for Xception65 (pascal_voc_seg.zip).</p> <p>The dataset consists of rough annotations used in weakly-supervised crack detection. It contains roughly annotated ground truths for the following datasets:</p> <ul> <li> <p><a href="https://www.irit.fr/~Sylvie.Chambon/AigleRN_GT.html">Aigle</a></p> </li> <li> <p><a href="https://github.com/cuilimeng/CrackForest-dataset">Crack Forest Dataset</a></p> </li> <li> <p><a href="https://github.com/yhlleo/DeepCrack">DeepCrack</a></p> </li> </ul> <p>Annotations of different "roughness" are stored. Directories suffixed "*_dil*" are synthetically-generated annotations, while directories suffixed "*_rough" and "*_rougher" are manually-generated annotations. The detail of the dataset is described in [1]. Please also refer to our GitHub repo <a href="https://github.com/hitachi-rd-cv/weakly-sup-crackdet">https://github.com/hitachi-rd-cv/weakly-sup-crackdet</a> for more details.</p> <p>This dataset is made available by Hitachi, Ltd.</p> <p>The pretrained model is used by [1]. Please use it for comparison experiments. Please refer to our GitHub repor for more details.</p> <p>[1] Inoue, Y., Nagayoshi, H.: Crack detection as a weakly-supervised problem: Towards achieving less annotation-intensive crack detectors. In: International Conference on Pattern Recognition (ICPR) (2020)</p>
Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems.
<p>The development of efficient sampling protocols for the capture of environmental DNA (eDNA) could greatly help improve accuracy of occupancy monitoring for species that are difficult to detect. However, the process of developing a protocol in situ is complicated for rare species by the fact that animal locations are often unknown. We tested sampling designs in lake and stream systems to determine the most effective eDNA sampling protocols for two rare species: the Sierra Nevada yellow-legged frog (<i>Rana sierrae</i>) and the foothill yellow-legged frog (<i>R. boylii</i>). We varied water volume, spatial sampling, and seasonal timing in lakes and streams; in lakes we also tested multiple filter types. We found that filtering 2 L versus 1 L increased the odds of detection in streams 5.42X (95% CI: 3.2-9.19X) in our protocol, from a probability of 0.51 to 0.85 per technical replicate. Lake sample volumes were limited by filter clogging and we found no effect of volume or filter type. Sampling later in the season increased the odds of detection in streams by 1.96X for every 30 days (95% CI: 1.3 - 2.97X) but there was no effect for lakes. Spatial autocorrelation of the quantity of yellow-legged frog eDNA captured in streams between 100 and 200 m, indicating that sampling at close intervals is important.</p>
Data from: Life-history theory provides a framework for detecting resource limitation: a test of the Nutritional Buffer Hypothesis
<p>For ungulates and other long-lived species, life-history theory predicts that nutritional reserves are allocated to reproduction in a state-dependent manner because survival is highly conserved. Further, as per-capita food abundance and nutritional reserves decline (i.e., density-dependence intensifies), reproduction and recruitment become increasingly sensitive to weather. Thus, the degree to which weather influences vital rates should be associated with proximity to nutritional carrying capacity—a notion that we refer to as the Nutritional Buffer Hypothesis. We tested the Nutritional Buffer Hypothesis using six moose (<i>Alces alces</i>) populations that varied in calf recruitment (33-69 calves/ 100 cows). We predicted that populations with high calf recruitment were nutritionally buffered against the effects of unfavorable weather, and thus were below nutritional carrying capacity. We applied a suite of tools to quantify habitat and nutritional condition of each population and found that increased browse condition, forage quality, and body fat were associated with increased pregnancy and calf recruitment, thereby providing multiple lines of evidence that declines in calf recruitment were underpinned by resource limitation. From 2001 to 2015, recruitment was more sensitive to interannual variation in weather (e.g., winter severity, drought) and plant phenology (e.g., duration of spring) for populations with reduced browse condition, forage quality, and body fat, suggesting these populations lacked the nutritional reserves necessary to buffer demographic performance against the effects of unfavorable weather. Further, average within-population calf recruitment was determined by regional climatic variation, suggesting that the pattern of reduced recruitment near the southern range boundary of moose stems from an interaction between climate and resource limitation. When coupled with information on habitat, nutrition, weather, and climate, life-history theory provides a framework to estimate nutritional limitation, proximity to nutritional carrying capacity, and impacts of climate change for ungulates.</p>
Data Visualization of Weight Sensor and Event Detection of Aifi Store
<p><a href="https://www.aifi.com/">Aifi</a> Store is an autonomus store for cashier-less shopping experience which is achieved by multi modal sensing (Vision modality, weight modality and location modality). Aifi Nano store layout (Fig 1) (Image Credits: <a href="https://dl.acm.org/doi/10.1145/3360322.3361018">AIM3S</a> research paper).</p> <p><strong>Overview:</strong><br> The store is organized in the gondola's and each gondola has shelfs that holds the products and each shelf has weight sensor plates. These weight sensor plates data is used to find the event trigger (pick up, put down or no event) from which we can find the weight of the product picked.</p> <p>Gondola is similar to vertical fixture consisting of horizontal shelfs in any normal store and in this case there are 5 to 6 shelfs in a Gondola. Every shelf again is composed of weight sensing plates, weight sensing modalities, there are around 12 plates on each shelf.</p> <p>Every plate has a sampling rate of **60Hz**, so there are 60 samples collected every second from each plate</p> <p>The pick up event on the plate can be observed and marked when the weight sensor reading decreases with time and increases with time when the put down event happens.</p> <p><strong>Event Detection:</strong></p> <p>The event is said to be detected if the moving variance calculated from the raw weight sensor reading exceeds a set threshold of (10000gm^2 or 0.01kg^2) over the sliding window length of 0.5 seconds, which is half of the sampling rate of sensors (i.e 1 second).</p> <p>There are 3 types of events:</p> <ol> <li>Pick Up Event (Fig 2)= Object being taken from the particular gondola and shelf from the customer</li> <li>Put Down Event (Fig 3)= Object being placed back from the customer on that particular gondola and shelf</li> <li>No Event = (Fig 4)No object being picked up from that shelf</li> </ol> <p><strong>NOTE:</strong></p> <ol> <li>1.The python script must be in the same folder as of the <em>weight.csv</em> files and .<em>csv</em> files should not be placed in other subdirectories.</li> <li>2.The videos for the corresponding weight sensor data can be found in the <strong>"Videos folder"</strong> in the repository and are named similar to their corresponding <strong>".csv"</strong> files.</li> <li>3.Each video files consists of video data from 13 different camera angles.</li> </ol> <p><strong>Details of the weight sensor files:</strong></p> <p>These weight.csv (Baseline cases and team particular cases ) files are from the AIFI CPS IoT 2020 week.There are over 50 cases in total and each file has 5 columns (Fig 5) (timestamp, reading (in grams), gondola, shelf, plate number).</p> <p>Each of these files have data of around 2-5 minutes or 120 seconds in the form of timestamp. In order to unpack date and time from timestamp use <em>datetime</em> module from python.</p> <p><strong>Details of the <em>product.csv</em> files:</strong></p> <p>There are <em>product.csv</em> files for each test cases and these files provide the detailed information about the product name, product location (gondola number, shelf number and plate number) in the store, product weight(in grams), also link to the image of the product.</p> <p><strong>Instruction to run the script:</strong></p> <p>To start analysing the weigh.csv files using the python script and plot the timeseries plot for corresponding files.</p> <ol> <li>Download the dataset.</li> <li>Make sure to place the python/ jupyter notebook file is in same directory as the .csv files.</li> <li>Install the requirements<br> <code>$ pip3 install -r requirements.txt</code></li> <li>Run the python script Plot.py<br> <code>$ python3 Plot.py</code></li> </ol> <p>After the script has run successfully you will find the corresponding folders of weight.csv files which contain the figures (weight vs timestamp) in the format</p> <p><strong>Instruction to run the Jupyter Notebook:</strong></p> <p>Run the Plot.ipynb file using Jupyter Notebook by placing .csv files in the same directory as the Plot.ipynb script.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> gondola_number,shelf_number.png</p> <p> Ex: 1,1.png (Fig 4) (<em>Timeseries Graph</em>)</p>
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.