Find research datasets worth reusing

On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes

We present an open-source database for evaluation of time synchronization algorithms for wireless acoustic sensor networks . More Information and examples on how to use the database can be found on our GitHub page: <a href="https://github.com/fgnt/paderwasn">https://github.com/fgnt/paderwasn</a>

opencc-by-4.0Sep 2024View details →

Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network

This Data and Software were used in the submitted paper  "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al. The data and software provided here are used to compute the velocity models with TomoTV. The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking. For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET. The same workflow was used for the picks from the permanent network. Data: - Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022. - Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022. - Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network. - Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...). Software (Hyp_format.py): -  Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper) -  Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format. -  Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118) -  Python packages needed: numpy, scipy, matplotlib, pandas, obspy

opencc-by-4.0Sep 2024View details →

Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD

This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD".

opencc-by-4.0Dec 2020View details →

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Background: The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.   Methods: First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis. Results: The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR. Conclusions: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/ distinction of muscle activity from background noise in sEMG signals.

opencc-zeroOct 2021View details →

Trophic rewilding benefits a tropical community through direct and indirect network effects

Species reintroductions can be used as a conservation strategy to restore ecological interactions and the functionality of impoverished ecosystems. The ecological effects of reintroductions go beyond restoring pairwise interactions, because reintroductions can change how extant species are indirectly linked to each other in an ecological community. These indirect pathways, in turn, may shape a myriad of ecological and evolutionary processes operating in ecological systems. Here, we investigated how reintroductions may affect the direct and indirect pathways connecting species in ecological networks. We modeled the potential effects of the reintroduction of four frugivore species (channel-billed toucans, red-humped agoutis, brown howler monkeys and yellow-footed tortoises) to the local seed dispersal network in an Atlantic Forest site, the Tijuca National Park (Tijuca, Brazil). We used a seed dispersal interaction dataset together with data on species occurrences in Tijuca to build network models. Then, we calculated how network structure and the total amount of indirect effects varied across simulated networks with and without the reintroduced species. Using random reintroduction simulations, we tested if the observed network changes were expected merely from the increase in species richness. The reintroduction of the frugivore species increased network connectance, nestedness, robustness, number of pathways and total amount of indirect effects in all simulated networks. The increase in number of pathways with the addition of the four reintroduced species was greater than the sum of isolated effects for each species, as some interaction pathways contained several reintroduced species. These changes in network metrics were significantly greater than if a randomly chosen set of four species were reintroduced. Furthermore, our results indicate that multiple reintroductions in the same area, known as refaunation, may have an even greater restoration effect than single species reintroductions through increased indirect connections in the network.

openother-openNov 2021View details →

globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive

Data in this archive are from the Collaborative databasing of North American bee collections within a global informatics network project. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside. Please use this citation for this archive: John Ascher, Digital Bee Collections Network data archive from the Collaborative databasing of North American bee collections within a global informatics network project. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853 This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a> ABSTRACT Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.

Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"

This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".

Supplementary dataset for Correlational networking guides the discovery of cryptic natural product biosynthetic enzymes

Supplementary files for the paper: Correlational networking guides the discovery of cryptic natural product biosynthetic enzymes 23777967_protease.fasta.xz:     A fasta file (compressed by xz) containing 23777967 protease sequences obtained from 161954 bacterial genomes 23777967_protease_cluster.csv.xz:     A csv file (compressed by xz) containing MMseqs2 cluster information of 23777967 proteases     This csv file has 5 columns: rep (representative sequence name), mem (member sequence name), number of members in cluster, cluster No. Fig1C_cytoscape.zip:     Cytoscape file corresponding to Fig.1C, as well as its node and edge tables Fig2C_cytoscape.zip:     Cytoscape file corresponding to Fig.2C, as well as its node and edge tables FigS2_cytoscape.zip:     Cytoscape file corresponding to Supplementary Fig.2, as well as its node and edge tables. Size was differently scaled for very large nodes containing more than 1000 precursors/proteases

Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm). The dataset is split into two parts: (1) Classification. The zip file veress_classification_raw_images.zip contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine) (2) Regression. The zip file veress_regression_raw_images.zip contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files S[1-8]_distance_measurement_20210803.xlsx.

opencc-zeroNov 2021View details →

Out from under the wing: reconceptualizing the insect wing gene regulatory network as a versatile, general module for body-wall lobes in arthropods

Body plan evolution often occurs through the differentiation of serially homologous body parts, particularly in the evolution of arthropod body plans. Recently, homeotic transformations resulting from experimental manipulation of gene expression have been interpreted as evidence that portions of dorsal and lateral arthropod body-wall are serially homologous to wings. These results, along with comparative data on the expression and function of genes in the wing regulatory network, provided a new perspective on an old question in insect evolution—how did the insect wing, evolve? A proposed ancestral role for the wing regulatory network in patterning body-wall margins motivated a broader comparison of gene function in wings and body-wall. We investigated the roles of a suite of ten wing- and body-wall related genes in a hemimetabolous insect, Oncopeltus fasciatus. Our results indicate that genes involved in wing development in O. fasciatus play similar roles in the development of adult body-wall flattened cuticular evaginations. We found extensive functional similarity between the development of wings and other bilayered evaginations of the body wall. Overall, our results support the existence of a versatile development module for building bilayered cuticular epithelial structures, which may have played a central role in the evolution of wings.

opencc-by-4.0Dec 2020View details →

Data belonging to: Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit

Dataset beloning to the publication: "Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit" in Ecology letters (2021). R script used to analyse the data in the .csv files

Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)

Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to  the LOON area (Gulf of La Spezia, Italy) Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) June 9-11, 17-18, 25-26 - 2021 ii) July 5-7, 21-27, 30-31 - 2021 iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021 Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) June 9-11, 17-18, 25-26 - 2021 ii) July 5-7, 21-27, 30-31 - 2021 iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021 Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) June 9-11, 17-18, 25-26 - 2021 ii) July 5-7, 21-27, 30-31 - 2021 iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021 SVP1 data  missing for  June 17-18 (2021) and July 5-7 (2021). Automatic Identification System (AIS) data for the ships transiting close to  the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for: i) June 9-11 - 2021 AIS recorded data not available after June 11, 2021 For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description",  Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available  at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001

opencc-zeroDec 2021View details →

The ability to disperse large seeds, rather than body mass alone, defines the importance of animals in a hyper-diverse seed dispersal network

1. Large-bodied animals play irreplaceable roles in seed dispersal, partly due to their capacity to disperse large seeds. Understanding this role at a community level has been limited by the paucity of network studies that include large vertebrates, and the almost complete absence of studies including synzoochoric dispersers. Synzoochoric dispersers can disperse seeds disproportionately large for their body size, potentially overlapping the roles of large-bodied animals. A comprehensive network, inclusive of large vertebrates and synzoochorous dispersers, is imperative to understand seed dispersal at a community level. 2. Here, we analysed the seed dispersal network of a hyper-diverse Sundaic forest in Malaysia using local ecological knowledge and including multiple forms of endozoochorous and synzoochorous dispersal. We evaluated the extent to which three disperser traits: body mass, seed-handling ability (size of the largest seed dispersed), and diet explained the importance of animals in the network. We evaluated dispersers' relative importance using four network metrics — degree of specialisation (nested rank), species strength, within-module connectivity (z-value), and between-modules connectivity (c-value). 3. We found that seed-handling ability had the biggest effect on a disperser's importance, with strong effects on three network metrics (species strength, ecological specialization, z-value) and moderate effects on connectivity between modules. Body mass was important in defining interactions within modules, and dietary differences defined the ecological specialisation of species in seed dispersal. 4. Important dispersers in our network were large-seed dispersers (e.g., rats, gibbons), large-bodied animals, in particular the Asian elephant, and animals with frugivorous diets such as hornbills. 5. Synthesis. Our work uncovers the significance of seed-handling ability in identifying pivotal seed dispersal roles in tropical rainforests. Key dispersers include large-bodied herbivores and medium-sized frugivores that could disperse large seeds by endozoochory, and smaller rodents that dispersed similar-sized seeds by synzoochory. Many of the species that emerged as particularly important for the seed dispersal network are currently threatened (e.g., the Asian elephant, gibbons, and hornbills). Their protection or reintroduction should be a top conservation priority.

opencc-by-4.0Sep 2021View details →

Exploring Kv1.2 channel inactivation through MD simulations and network analysis

MD equilibration trajectories of Kv1.2 WT and mutants in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file (.prmtop).

opencc-by-4.0Dec 2021View details →

Latent space images and related deep neural network for the BAGLS dataset

In this repository, we provide the latent space images for the BAGLS (<a href="https://www.nature.com/articles/s41597-020-0526-3">Gómez, Kist et al., Sci Data 2020</a>, available at <a href="https://bagls.org/">www.bagls.org</a>) training dataset. We further provide a pre-trained deep neural network for glottis segmentation having only a single latent space, i.e. a latent space image, and no skip connections.

opencc-by-4.0Dec 2021View details →

EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks

EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs. The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.

opencc-by-4.0Dec 2021View details →

Supplementary materials for "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations"

This repository contained supplementary materials in the study named: "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations." For extracting all files from the downloaded tar.gz file, the following commanda could be used: <pre><code>tar -xf supplementary_materials.tar.gz </code></pre> The supplementary_materials/data diretcory contains the following sections: <ol> <li>eqtm_predictions: This contains the training and testing datasets for the eQTM prediction procedures</li> <li>public_methylation_data_and_pca: This contains the harmonized public DNA methylation dataset and its first 100 PCA components</li> <li>cca_data: This contains the CCA components for the public DNA methylation and gene expression datasets for the negative eQTMs, and the input datasets for the functional gene pair prediction analylsis.</li> <li>gene_enrichment_results_for_cca: This contains the gene enrichment results for the CCA components for negative and positive eQTMs</li> </ol> The supplementary_materials/model directory contains the following sections: <ol> <li>disease_tissue_predictions: This contains the models trained for tissue prediction and disease prediction based on the PCA components from the public DNA methylation data</li> <li>eqtm_predictions: This contains models trained for eQTM prediction</li> <li>cca_transformations: This contains CCA transformation models and models for STRING gene pair predictions  </li> </ol>

openother-openDec 2021View details →

Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production

Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production. It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a>

opencc-by-4.0Dec 2020View details →

From mass spectral features to molecules in molecular networks: MolNotator LDB dataset.

Finding actual molecules in LC-MS/MS experiments can prove challenging due to the considerable amount of redundant ions generated during ionization. In this context, MolNotator was created and validation with this dataset. MolNotator is a Python 3.7 package designed to predict molecules (molecular masses) by combinatorial triangulation in LC-MS/MS experiments after a preprocessing step using MZMine. An MGF and a CSV files output from MZmine are required as input for MolNotator which are placed in the "mzmine_out" folder of the project folder (the uploaded dataset). Instructions for the use of MolNotator are available on GitHub (https://github.com/ZzakB/MolNotator), Pypi (https://pypi.org/project/MolNotator/) and in the associated publication. The dataset consists of 193 LC-MS/MS analyses of lichen pure standards (previously used for the Lichen Database, LDB), 156 of which were detected by manual curation and served to benchmark MolNotator. Results indicated more than 90% of the 156 molecular masses were predicted by MolNotator under 2 ppm error on average. The project folder contains, in addition to the mzmine_out folder, a database and a params folder (see GitHub and Pypi) as well as a styles folder, containing different styles that can be imported on Cytoscape (https://cytoscape.org/) to visualise MolNotator's network output.