Find research datasets worth reusing

Satellite-derived chlorophyll-a concentrations for Lake Mulargia (Sardinia, Italy) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Mulargia (Sardinia, Italy) for the period 29 Mar. 2013 - 31 Jan. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England

Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes. Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance. Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network. Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.

Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series

This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream. Preprocessing The following operations have been applied on the original S5P imagery: <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks. Data Format and Naming Conventions Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions: <ul> <li>Files starting with X represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with Y represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with MASK</li> <li>Files starting with VALMASK contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> Numbers in filenames encode spatial and temporal block indexes. In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models: <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> References [1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853. [2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.

The transcription factor network of E. coli steers global responses to shifts in RNAP concentration

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

openmit-licenseJul 2022View details →

Neural network models for processing of ultrasound rodents recordings

Neural network models for processing of ultrasound rodents recordings to be used with custom software dedicated to detection and classification of USV.

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. 3. The networks could predict the location of future forest loss to a high degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.

opencecill-2.1Apr 2022View details →

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

NXTensor extraction library, experiment code, tropical cyclone and background images and their metadata generated from the meterological reanalysis ERA5 and MERRA-2 according to the HURDAT2 cyclone tracks. Version specifications: <ul> <li>NXTensor: v0.3.3.10</li> <li>Experiment code: v2.0.3</li> <li>Image sets: v1</li> </ul>

Resolving higher-level phylogenetic networks with repeated hybridization in a complex of polytypic salamanders (Plethodontidae: Desmognathus)

Repeated hybridization between incipient lineages is a common feature of ecological speciation and ecomorphological diversification. However, computational constraints currently limit our ability to reconstruct network radiations from gene-tree data. Available methods are limited to level-1 networks wherein reticulations do not share edges, and higher-level networks may be non-identifiable in many cases. We present a heuristic method to recover information from higher-level networks across a range of potentially identifiable empirical scenarios, supported by a theorem and success in simulated data. When extrinsic information indicating the location and direction of recent or ancestral hybridization events is available, our method can yield successful estimates of non-level-1 networks, or at least a reduced possible set thereof. We apply this technique to the Pisgah clade of Desmognathus salamanders, which contains four to seven species exhibiting two discrete phenotypes, aquatic "shovel-nosed" and semi-aquatic "black-bellied" forms in the southern Appalachian Mountains of the eastern United States. Phylogenomic data strongly support a single backbone topology with up to five overlapping hybrid edges. These results suggest an unusual mechanism of ecomorphological hybrid speciation, wherein a binary threshold trait causes hybrids to shift between two microhabitat niches, promoting ecological divergence between sympatric hybrids and parentals. This contrasts with other well-known systems in which hybrids exhibit intermediate, novel, or transgressive phenotypes. Geographically proximate populations of both phenotypes exhibit admixture, and at least two black-bellied lineages have been produced via reticulations between shovel-nosed parentals, suggesting complex transmission dynamics. The genetic basis of these phenotypes is unclear and further data are needed to clarify the nature of selection and speciation in the group.

Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples

Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox

Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples

Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox

Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks

This dataset contains the following: <ul> <li>data: <ul> <li>preprocessed: hourly European weather radar data (here: radar) and aggregated simulation outputs (here: abm), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li>shapes: geographical shapes used for plotting</li> </ul> </li> <li>results: trained models and corresponding results for both simulated data (here: abm) and weather radar data (here: radar).</li> <li>figures: final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.

opencc-by-4.0Jul 2021View details →

The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data

Dataset used in the research presented in the article: Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" Materials 15, no. 10: 3727. https://doi.org/10.3390/ma15103727 The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.

Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET)

This dataset contains local earthquake coda waveform and pre-signal noise waveforms from the Jammu And Kashmir Seismological NETwork (JAKSNET), a joint endeavor between the Indian Institute of Science Education and Research Kolkata (IISER-K), Shri Mata Vaishno Devi University (SMVD) and the University of Cambridge, UK. The network was initiated in July 2013 and comprised 24 broadband seismograph systems deployed across the J&K Himalaya. A total of 696 vertical component coda waveforms, from 121 small-to-moderate local earthquakes of magnitude between 3.0 and 5.5, within the epicentral distance of 200 km are provided in this database. Pre-signal representative noise from 22 stations, which recorded these earthquakes, have been provided for computing signal-to-noise ratio for the coda signal. The waveform data is sampled at 100 samples per second (sps), are corrected for instrument response, and filtered in the frequency band of 0.02 to 30 Hz. Coda waveforms start from twice the S-wave arrival time and are of 90 s duration. This data has been used to compute the seismic coda-wave attenuation of the Jammu and Kashmir Himalaya. The manuscript is submitted for review in JGR Solid Earth and this dataset complements the manuscript.

opencc-by-4.0Oct 2021View details →

Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.

This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022 This repository contains: Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis. Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data. Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)

opencc-by-4.0Aug 2022View details →

Dataset for Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting

The dataset contains time series of surface soil moisture at three locations, namely Idaho, Indiana, and Oklahoma. For each location, the folder contains multiple gif images, one for each year, each containing 12 frames corresponding to monthly averages. More information at  https://github.com/gtsagkatakis/ETCN

opencc-by-4.0Sep 2018View details →

Estimates of tidal-marsh bird densities using Bayesian networks

This data set was generated by Wiest, WA, MD Correll, BG Marcot, BJ Olsen, CS Elphick, TP Hodgman, GR Gunterspergen, and WG Shriver. 2019. Estimates of tidal marsh bird densities using Bayesian networks. Journal of Wildlife Management 83:109-120 (paper available here <a href="https://doi.org/10.1002/jwmg.21567">https://doi.org/10.1002/jwmg.21567</a>) and was developed by the Saltmarsh Habitat and Avian Research Program (https://www.tidalmarshbirds.org/). The same data set has subsequently been used in other papers produced by our group.

The frugivory network between birds and plants in a Neotropical periurban park

Frugivory networks exhibit a set of properties characterized by a number of network-theory derived metrics. Their structure often form deterministic patterns that can be explained by the functional roles of interacting species. Although we know lots about how these networks are organized when ecosystems are in a complete, functional condition, we know much less about how incomplete and simplified networks (such as those found in urban and periurban parks) are organized, which features are maintained, which ones are not, and why. In this paper, we examine the properties of a network between frugivorous birds and plants in a small Neotropical periurban park. We found a frugivory network composed of 29 species of birds and 23 of plants. The main roles in this network are played by four species of generalist birds (three resident, one migratory: Myiozetetes similis, Turdus grayi, Chlorospingus flavopectus, and Dumetella carolinensis) and three species of plants (one exotic, two early successional: Phoenix canariensis, Phoradendron sp., and Witheringia stramoniifolia). When compared to reference data from other locations in the Neotropics, species richness is low, one important network-level metric is maintained (modularity) whereas another one is not (nestedness). Nestedness, a metric associated with network specialists, is a feature this network lacks. Species-level metrics such as degree, species strength, and module roles, are not maintained. Our work supports modularity as the most pervasive network-level metric of altered habitats. From a successional point of view, our results suggest that properties revealed by species-level indices may be developed at a later time, lagging the acquisition of structural elements.

Seasonal variation in impact of non-native species on tropical seed dispersal networks

Invasive non-native species can alter animal-mediated seed dispersal interactions and ultimately affect the stability of recipient communities. The degree of such disturbances, however, is highly variable and depends on several factors, two of which have received little attention: the relative timing of native and non-native fruiting phenologies, and the associated variation in relative resource availability across the fruiting period. Both are likely to alter plant-seed disperser interactions threatened by biological invasions. Here we investigated the impact of plant invasions on the seasonal dynamics of frugivory and seed dispersal networks across a large-scale experimental setup and a plant invasion gradient on a tropical island. We recorded fruit and frugivore abundances, and plant-frugivore interactions across 8 inselbergs (i.e. rocky outcrops) with different levels of plant invasion during 10 months on the island of Mahé, Seychelles. By combining four sampling methods of plant-frugivore interactions we constructed quantitative seed dispersal networks at all sites across two 5-month seasons: the on-peak and off-peak fruiting season. Our findings showed that, by fruiting mostly synchronously with natives, non-native plants compete with natives for dispersal services, predominantly carried out by native frugivores. Variation in native seed dispersal was driven by plant invasion and seasonality. Specifically, native seed dispersal declined with the degree of invasion; dispersal frequency increased with fruit abundance more strongly during the off-peak fruiting season; and networks became increasingly specialised during off-peak. These results indicated that during the main fruiting peak seed dispersal services were saturated, which likely intensified the competition between native and non-native fruits. When resources were scarce during off-peak fruiting season, native and non-native frugivores were more selective in their fruit choice at sites dominated by non-native plants. We showed that native plant and frugivore populations and native seed dispersal interactions were more vulnerable in invaded plant communities, where non-native plants compete with natives for dispersal services potentially reducing native recruitment. As invasive non-native plants dominate many ecosystems worldwide, particularly on islands, our findings showed that controlling plant invasions in vulnerable native communities can be critical to maintain native ecosystem functions and biodiversity.

A study of the spatial correlation network structure of urban innovation in Guangdong

Based on the modified gravity model, a spatial correlation network of innovation was constructed among cities in Guangdong, China. Social network analysis was employed to explore their evolution characteristics during 2009–2017. The results indicate that the innovation output of prefecture-level cities in Guangdong Province shows both spatial correlations and differences. Their network shows lower density, higher efficiency, and rigid stratification properties. Based on small cluster analysis, these cities are classified into four blocks, the members of which changed. In 2017, four well-defined subgroups formed, which are "bidirectional spillover plate", "main spillover plate", "net beneficial plate", and "agent plate". With this network, the geographical characteristics of the innovation capabilities and differences among the cities in Guangdong, as well as the different positions and roles of each city in the associated network, can be properly understood. Consequently, the transmission mechanisms and development strategies of innovation in Guangdong Province can be better explored.

Preserved Specimen Records with Still Images Registered Across Biodiversity Data Networks in Period 2019-2022 hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86

Natural History Collections keep physical specimen of organisms. Some of these collections provide digital catalogs. These digital catalogs may be registered with, and tracked by, digital registries like Global Biodiversity Information Facility (GBIF, https://gbif.org), Integrated Digitized Biocollections (iDigBio, https://idigbio.org), and the Biological Collection Access Service (BioCASe, https://biocase.org). This publication reports on the availability of registered preserved specimen records with at least one associated image (i.e. StillImage) over period 2019/2022.  For a description of the methods, see: Jorrit Poelen. (2022). jhpoelen/specimen-image-index: 0.0.1 (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.7032668 The associated provenance log identifiers include: hash://sha256/251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417 hash://sha256/261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12 hash://sha256/8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead hash://sha256/9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e hash://sha256/b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86 where the hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86 is cryptographically linked to the others.  For completeness, the associated provenance logs are included using their hex hashes as file attached to this publication: 251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417 261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12 8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead 9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86 Other files include, suffixed by the first four hex characters of their associated provenance log id: # this file   README # identifiers of tracked content captured in associated provenance logs. content_251f.tsv content_2611.tsv content_8ff4.tsv content_9a41.tsv content_b986.tsv content_da74.tsv # identifiers of tracked content said to contain multimedia content-with-multimedia_251f.tsv content-with-multimedia_2611.tsv content-with-multimedia_8ff4.tsv content-with-multimedia_9a41.tsv content-with-multimedia_b986.tsv content-with-multimedia_da74.tsv # identifiers of tracked content said to contain multimedia and still images content-with-still-images_251f.tsv content-with-still-images_2611.tsv content-with-still-images_8ff4.tsv content-with-still-images_9a41.tsv content-with-still-images_b986.tsv content-with-still-images_da74.tsv # identifiers of tracked content said to contain multimedia, still images, and specimen content-with-still-images-and-specimen_251f.tsv content-with-still-images-and-specimen_2611.tsv content-with-still-images-and-specimen_8ff4.tsv content-with-still-images-and-specimen_9a41.tsv content-with-still-images-and-specimen_b986.tsv content-with-still-images-and-specimen_da74.tsv # reported scientificName associated with preserved specimen record with image(s) content-name-image_251f.tsv.gz content-name-image_2611.tsv.gz content-name-image_8ff4.tsv.gz content-name-image_9a41.tsv.gz content-name-image_b986.tsv.gz content-name-image_da74.tsv.gz # names associated with preserved specimen with images grouped in categories Plantae, Insecta and Mammalia plantae_insecta_or_mammalia_image_251f.tsv.gz plantae_insecta_or_mammalia_image_2611.tsv.gz plantae_insecta_or_mammalia_image_8ff4.tsv.gz plantae_insecta_or_mammalia_image_9a41.tsv.gz plantae_insecta_or_mammalia_image_b986.tsv.gz plantae_insecta_or_mammalia_image_da74.tsv.gz # associated discussions github_bio-guoda_preston_issues_168-2022-08-29.txt github_jhpoelen_specimen-image-index_issues_1-2022-08-29.txt # associated processing scripts make-all.sh make.sh has-multimedia.sh extract-still-image-records.sh has-still-image.sh extract-preserved-specimen-records.sh has-specimen.sh create-named-specimen-with-image-table.sh