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9,302 results for “DETECT”
Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks
<p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p> <ul> <li> <p>Pollen images of 16 types: <code>images_16_types.zip</code></p> <ul> <li>Acer Pseudoplatanus</li> <li>Aesculus Carnea</li> <li>Alnus</li> <li>Anthoxanthum</li> <li>Betula Pendula</li> <li>Brassica</li> <li>Carpinus</li> <li>Corylus</li> <li>Dactylis Glomerata</li> <li>Fraxinus</li> <li>Pinus Nigra</li> <li>Platanus</li> <li>Populus Nigra</li> <li>Prunus Avium</li> <li>Sequoiadendron Giganteum</li> <li>Taxus Baccata</li> </ul> </li> <li> <p>Pollen video library <code>pollen_video_library.zip</code></p> <ul> <li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li> <li>In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].</li> </ul> </li> <li> <p>Field data over 3 days are gathered in Graz in spring 2020. <code>pollen_field_data.zip</code></p> </li> <li> <p>Sample code to load the data and visualize the images is in <code>plot_pollen_sample.py</code>. Download and extract the file <code>images_16_types.zip</code> in the same folder as <code>plot_pollen_sample.py</code> to run the example.</p> </li> </ul> <p> </p> <p><strong>Dependecies</strong></p> <ul> <li>opencv</li> <li>numpy</li> <li>matplotlib</li> </ul> <p> </p> <p><strong>Credit</strong></p> <p>[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119.</p> <pre><code>@inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, } </code></pre>
Ashby et al., Simultaneous detection of Bluetongue virus serotypes using xMAP technology
<p>Figure S1: Performance of the BTV 9-plex xMAP assay in detection of BTV reference strains BTV 1-24. Figure S2 Percentage inhibition of BTV-24 amplification in RT-qPCR in the presence of individual (<strong>a</strong>) or multiple (<strong>b</strong>) primers for other serotypes. Figure S3. Performance of the BTV xMAP assay to detect BTV reference strains BTV 1-24. Table S1: Primers and capture probes used in the xMAP assay. Table S2. Composition of the BTV serotyping 9-plex panel. Table S3: Composition of the BTV serotyping 5-plex panel. Table S4: Results of samples tested using group specific RT-qPCR BTV assay</p>
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
<p>The .xlsx dataset includes all patients used for training, internal-external and external validation: these can be distinguished by looking at the ID (first column) in the dataset: those in format Axxxx-<Date> are the data used for the training, those in the format 20xx are the data used for the internal-external validation, while the remaining data were used for external validation.</p> <p>As regards the features: for the Target feature the value 1 stands for "Positive to COVID-19" while the value 0 stands for "Negative to COVID-19"; while for the Sex feature the value 1 stands for "Male" while the value 0 stands for "Female".</p> <p>The full article is available at: https://www.degruyter.com/view/journals/cclm/ahead-of-print/article-10.1515-cclm-2020-1294/article-10.1515-cclm-2020-1294.xml.</p> <p>A pre-print version of the article is also available on MedrXiv: https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1</p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong> The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. </p> <p><strong>Methods</strong> Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.</p> <p><strong>Results</strong> We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 15 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC 16 from 0.75 to 0.78; and specificity from 0.92 to 0.96. </p> <p><strong>Conclusions</strong> ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.</p>
A semi-automated iterative process for detecting feature interactions
<p>Apresentação do artigo: "A semi-automated iterative process for detecting feature interactions" para trilha de Research do SBES 2020.</p> <p>Abstract: For configurable systems, features developed and tested separately may present a different behavior when combined in a system. Since software products might be composed of thousands of features, developers should guarantee that all valid combinations work properly. However, features can interact in undesired ways, resulting in failures. A feature interaction is an unpredictable behavior that cannot be easily deduced from the individual features involved. We proposed VarXplorer to inspect feature interactions as they are detected and incrementally classify them as benign or problematic. Our approach provides an iterative analysis of feature interactions allowing developers to focus on suspicious cases. In this paper, we present an experimental study to evaluate our iterative process of tests execution. We aim to understand how VarXplorer could be used for a faster and more objective feature interaction analysis. Our results show that VarXplorer may reduce up to 50% the amount of interactions a developer needs to check during the testing process.</p>
Figure 1 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 1 Study area. Sampling sites of octopuses from the Revillagigedo Archipelago.
Figure 2 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 2 Ocellus of specimen No. 48 A defrosted B preserved in 96% ethanol.
Real-Time Early Detection and Monitoring of Flooding Using Low-Cost Highly Sensitive Ultrasound Sensing of Water Level
<p>Flooding poses safety hazards to motorists, emergency and maintenance crews and may cause costly damage to transportation infrastructure and its operation. Flash flooding, in particular, causes the most flood-related deaths According to NOAA, in 2017 alone, flash flooding also caused $60.7 billion worth of economic damage. Low-water crossings are among the first places where deaths and significant damages to vehicles occur during flooding. With flash flooding, when a critical corridor is blocked by a high level of water, it affects the safety of the general public. To keep the critical corridors open as long as possible, and to minimize losses from flooding, accurate early detection of the rising water level is essential. The flood level detection system has to have flood stage maps in the geographic information system for the street-, roadway-, and critical-freight corridors. This area encompasses public roads in urbanized areas that provide access and connection to the primary roads for ports, public transportation or other transportation facilities. The main goal of this project is to develop cost-effective and high efficient solar-powered water level detection units and implement real-time water level monitoring for water both pavement and river stream. The project was performed with three objectives: to develop the low cost reliable real-time data of the ultrasound water level detection system, increasing its data reliability and resolution; 2) to improve an energy-saving processing system; and to deploy the ultrasound water level detection system and real-time monitoring system for water both pavement and river stream for safety measures. The obtained results and findings imply the developed monitoring system can present reliable water level data with efficient power and data transmission system.</p>
Enhancing Evaluation of Wildlife Detection Systems
<p>Every year in the United States, wildlife-vehicle collisions (WVCs) cause 200 human fatalities, 26,000 human injuries, and substantial harm to wildlife populations, resulting in approximately $8.4 billion in total costs. The research team examined two US 64 bridges located near Lumberton, New Mexico that were designed to allow wildlife to cross underneath. Monitoring stations were positioned at each of the crossings so that both wildlife approaches and passages were observed. Special mounting brackets were designed and fabricated to allow for the installation of monitoring equipment. Wildlife observations were supplemented with WVC counts. Over seven months of study, nearly 100,000 wildlife photos were captured consisting of 1,438 individual animals using the crossing structures. Both crossings saw passage rates of approximately 80%. Findings suggest that elk and deer used both the smaller and larger crossings. Elk predominated during December through March while deer predominated during May through June, with most crossings occurring during defined nighttime peaks. There have been no elk and fewer deer collisions since the wildlife crossing system was installed. WVCs that have occurred were near the ends of the wildlife fencing, suggesting that fencing extent may be a factor that warrants further research. Lessons learned through this project help develop our understanding of WVCs, contributing to our goal of saving lives – both human and wildlife – and enhancing wildlife conservation efforts.</p>
Constraints from gravitational waves detections of binary black hole mergers on the C12(alpha, gamma)O16 rate
<p><strong>Reproduction package for the paper "Constraints from gravitational wave detections of binary black hole mergers on the $^{12}\rm{C}\left(\alpha,\gamma\right)^{16}\!\rm{O}}$ rate"</strong></p> <p> </p> <p><strong>This package contains inlists for MESA, custom reaction rates used, and processed output data.</strong></p> <p> </p> <p><strong>Version 1 contains files for the original arxiv submission.</strong></p> <p><strong>Version 2 contains updates due to changes made during peer review</strong></p> <p><strong>Version 3 updates inlist_ppisn. There is a slight difference between the inlists used for different parts of the paper</strong></p>
Datasets and models for Object detection
<p>This repository includes the datasets and models for object detection. </p> <p>The "Datasets" folder includes the training and testing datasets for object detection.( Both Tf-record and Images are provided)</p> <p>The models includes a pre-trained model from TensorFlow 2 model zoo and our trained model. The "Custom_model" folder includes the training log and check points.</p>
Data from: Pulmonary nodule detection in patients with a primary malignancy using hybrid PET/MRI: is there value in adding contrast-enhanced MR imaging?
Purpose: To investigate the added value of post-contrast VIBE (volumetric-interpolated breath-hold examination) to PET/MR imaging for pulmonary nodule detection in patients with primary malignancies. Materials and Methods: This retrospective institutional review board–approved study, with waiver of informed consent, included 51 consecutive patients who underwent 18F-fluorodeoxyglucose (FDG) PET/MR followed by PET/CT for cancer staging. In all patients, the thorax was examined with pre-and post-contrast VIBE MR with simultaneous PET acquisition. Two readers blinded to the patients' data independently recorded their level of suspicion for pulmonary nodules based on PET, pre-contrast VIBE, and fused PET/MR images (first session), and reassessed them 4-weeks later after addition of post-contrast VIBE (second session). Jackknife alternative free-response receiver-operating-characteristic (JAFROC) analysis was performed, with PET/CT as the reference standard. Results: A total of 151 pulmonary nodules (44 FDG-avid, 107 non-FDG-avid nodules) were detected on PET/CT, including 62 nodules≥5mm in diameter and 89 nodules<5mm. In the first session, the average nodule detection rate was 53.3% for all nodules, 97.7% for FDG-avid, 35.0% for non-FDG-avid nodules, 87.9% for nodules≥5mm and 29.2% for nodules<5mm. In the second session, the average detection rate was 53.3% for all nodules, 97.7% for FDG-avid, 35.0% for non-FDG-avid nodules, 85.5% for nodules≥5mm and 30.9% for nodules<5mm. The average JAFROC figure-of-merit was 0.837 in the first session and 0.848 in the second session. There were no significant differences in detection performance between sessions (P=0.48). Conclusion: The addition of post-contrast VIBE to hybrid PET/MR imaging provided no additional value in the detection of pulmonary nodules.
Data from: A framework for detecting natural selection on traits above the species level
To what extent can natural selection act on groupings above the species level? Despite extensive theoretical discussion and growing practical concerns over increased rates of global ecological turnover, the question has largely evaded empirical resolution. A flexible and robust hypothesis-testing framework for detecting the phenomenon could facilitate significant progress in resolving this issue. We introduce a permutation-based approach, implemented in the R package perspectev, which provides an explicit test of whether empirical patterns of correlation between upper level trait values and survivorship are reducible to correlations manifested at lower levels. The package is applicable to virtually any nested set of upper- and lower level groupings, a wide variety of upper level traits, and both historical and contemporary occurrence data. We apply this approach to five paleontological data sets that represent different magnitudes of extinction and differ in taxonomic breadth, geological timing and geographic extent. Using simulations, we demonstrate that this method is a robust means of detecting irreducibility in the relationship between upper level traits and survivorship, and outline circumstances in which the method is less effective. We also find evidence consistent with previous findings of selection above the species level for geographic range size in North American K-Pg molluscs and show that this phenomenon was evident for the same molluscan genera globally. Ultimately, we conclude that at certain points in history, some higher level taxonomic groups have survived differentially with respect to geographic range size in a manner that is not explained by the same trait at the species level, and we show that evidence for this phenomenon varies across taxa and extinction events. We release our method as a flexible and easy-to-use R package that will allow others to help determine the relative frequency of this macroevolutionary phenomenon, both in the fossil record and in estimates of contemporary extinction risk.
Data from: Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection
Identifying local adaptation is crucial in conservation biology in order to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so-called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FSTs, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms.
Data from: Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling
Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.
Data from: A cross-lingual similarity measure for detecting biomedical term translations
Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks.
Data from: Rapid and accurate detection of urinary pathogens by mobile IMS-based electronic nose: a proof-of-principle study
Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) –based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. In conclusion: These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.
Data from: Assessment of intrathecal free light chain synthesis: comparison of different quantitative methods with the detection of oligoclonal free light chains by isoelectric focusing and affinity-mediated immunoblotting
Objectives: We aimed to compare various methods for free light chain (fLC) quantitation in cerebrospinal fluid (CSF) and serum and to determine whether quantitative CSF measurements could reliably predict intrathecal fLC synthesis. In addition, we wished to determine the relationship between free kappa and free lambda light chain concentrations in CSF and serum in various disease groups. Methods: We analysed 166 paired CSF and serum samples by at least one of the following methods: turbidimetry (Freelite™, SPAPLUS), nephelometry (N Latex FLC™, BN ProSpec), and two different (commercially available and in-house developed) sandwich ELISAs. The results were compared with oligoclonal fLC detected by affinity-mediated immunoblotting after isoelectric focusing. Results: Although the correlations between quantitative methods were good, both proportional and systematic differences were discerned. However, no major differences were observed in the prediction of positive oligoclonal fLC test. Surprisingly, CSF free kappa/free lambda light chain ratios were lower than those in serum in about 75% of samples with negative oligoclonal fLC test. In about a half of patients with multiple sclerosis and clinically isolated syndrome, profoundly increased free kappa/free lambda light chain ratios were found in the CSF. Conclusions: Our results show that using appropriate method-specific cut-offs, different methods of CSF fLC quantitation can be used for the prediction of intrathecal fLC synthesis. The reason for unexpectedly low free kappa/free lambda light chain ratios in normal CSFs remains to be elucidated. Whereas CSF free kappa light chain concentration is increased in most patients with multiple sclerosis and clinically isolated syndrome, CSF free lambda light chain values show large interindividual variability in these patients and should be investigated further for possible immunopathological and prognostic significance.
Data from: Birt-Hogg-Dube syndrome prospectively detected by review of chest computed tomography scans
Purpose: Birt-Hogg-Dube syndrome (BHD) is a rare disorder caused by mutations in the gene that encodes folliculin (FLCN) and is inherited in an autosomal dominant manner. BHD is commonly accompanied by fibrofolliculomas, renal tumors, multiple pulmonary cysts, and spontaneous pneumothorax. The aim of this study was to detect BHD prospectively in patients undergoing chest computed tomography (CT) scans and to evaluate further the characteristics of BHD in Korea. Methods: We prospectively checked and reviewed the chest CT scans obtained for 10,883 patients at Gangnam Severance Hospital, Seoul, Korea, from June 1, 2015 to May 31, 2016. Seventeen patients met the study inclusion criteria and underwent screening for FLCN mutation to confirm BHD. We analyzed the characteristics of the patients confirmed to have BHD and those for a further 6 patients who had previously been described in Korea. Results: Six (0.06%) of the 10,883 patients reviewed were diagnosed with BHD. There was no difference in demographic or clinical features between the patients with BHD (n=6) and those without BHD (n=11). Pneumothorax was present in 50% of the patients with BHD but typical skin and renal lesions were absent. The maximum size of the cysts in the BHD group (median 39.4 mm; interquartile range [IQR] 11.4 mm) was significantly larger than that in the non-BHD group (median 15.8 mm; IQR 7.8 mm; P = 0.001). Variable morphology was seen in 100.0% of the cysts in the BHD group but in only 18.2% of the cysts in the non-BHD group (P = 0.002). Nine (95%) of the total of 12 Korean patients with BHD had experienced pneumothorax. Typical skin and renal lesions were present in 20.0% of patients with BHD. Conclusions: Our findings suggest that BHD can be detected if chest CT scans are read in detail.
Data from: Cophylogenetic signal is detectable in pollination interactions across ecological scales
That evolutionary history can influence the way that species interact is a basic tenet of evolutionary ecology. However, when the role of evolution in determining ecological interactions is investigated, focus typically centers on just one side of the interaction. A cophylogenetic signal, the congruence of evolutionary history across both sides of an ecological interaction, extends these previous explorations and provides a more complete picture of how evolutionary patterns influence the way species interact. To date, cophylogenetic signal has most typically been studied in interactions that occur between fine taxonomic clades that show high intimacy. In this study, we took an alternative approach and made an exhaustive assessment of cophylogeny in pollination interactions. To do so, we assessed the strength of cophylogenetic signal at four distinct scales of pollination interaction: (1) across plant–pollinator associations globally, (2) in local pollination communities, (3) within the modular structure of those communities, and (4) in individual modules. We did so using a globally distributed dataset comprised of 54 pollination networks, over 4000 species, and over 12,000 interactions. Within these data, we detected cophylogenetic signal at all four scales. Cophylogenetic signal was found at the level of plant–pollinator interactions on a global scale and in the majority of pollination communities. At the scale defined by the modular structure within those communities, however, we observed a much weaker cophylogenetic signal. Cophylogenetic signal was detectable in a significant proportion of individual modules and most typically when within-module phylogenetic diversity was low. In sum, the detection of cophylogenetic signal in pollination interactions across scales provides a new dimension to the story of how past evolution shapes extant pollinator–angiosperm interactions.
Data from: ConfeitoGUI: a toolkit for size-sensitive community detection from a correlation network
Analysis of the large amounts of data accumulated in public databanks can facilitate a more comprehensive understanding of molecular biological processes. Community detection from molecular biological data is paramount in characterizing evolutionary and functional traits of organisms based on gene homology and co-expression, respectively. Although there are common tools to detect local communities from a large network, no toolkit exists for detecting communities that include an element of interest based on size sensitivity, i.e., functionality to obtain local communities with preferred sizes. Herein, we present the ConfeitoGUI toolkit for detecting local communities from a correlation network involving size sensitivity. We compared the toolkit with other common tools for detection in reconstructing communities of microarray experiments of mice. In the results, ConfeitoGUI was observed to be preferable for detecting communities whose sizes are similar to those of original communities compared to other common tools. By changing simple parameters representing sizes for the toolkit, a user can obtain local communities with preferred sizes, which is beneficial for further analysis of members belonging to the communities.
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.