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opencc-by-4.0Dec 2022View details →

Radiometer network dataset of 10 Hz spectral irradiance and derived variables (LIAISE campaign)

Description Measurements from the spatial network of 15 radiometers deployed at the LIAISE field campaign in 2021. 14 July 2021 until 28 July 2021. Dataset Contents <ul> <li>rsds: Total shortwave downwelling solar irradiance @ 10 Hz, 1 sec, and 1 minute resolution (Level 2, derived dataset)</li> <li>prw: Total column integrated water vapour @ 1 sec resolution (Level 2, derived dataset)</li> <li>spectrum: Pre-calibrated solar spectral irradiance measurements @ 10 Hz resolution (Level 1, source dataset)</li> <li>raw data: straight from the sensors (Level 0)</li> </ul> Dataset Quality All data is quality controlled and completed with metadata and quality flags. Level 2 data is calibrated against high quality references, and derived from Level 1 data. Methodology, performance, and usage all described in detail in an upcoming pre-print. References and more info <ul> <li><a href="https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/">Radiometer reference paper (FROST)</a></li> <li>LIAISE <a href="https://liaise.aeris-data.fr/">campaign website</a>, <a href="https://liaise.aeris-data.fr/page-catalogue/?uuid=594dbc5d-986e-4679-b0ec-7d1e29b5cab9">LIAISE database</a></li> <li>Dataset description paper: <a href="https://arxiv.org/abs/2307.06980">pre-print on Arxiv</a></li> <li>Code to produce these data <a href="https://doi.org/10.5281/zenodo.10159129">on Zenodo</a></li> </ul> Version History v1.2: fixed bug of incorrect wavelength labeling, all bands but 900 and 940 nm were affected. This only concerns the 10 Hz spectrum data, rest unchanged. v1.1: added raw (level 0) data, fixed typo in file name for the 10 Hz spectrum zip (L2 -> L1).

Open Access on GNSS Permanent Networks Data in Case of Disaster

Earthquakes, as a natural phenomenon causing large physical and social destruction, are the subject of intensive research throughout the world. Spurred by the fact that in year 2020, two catastrophic earthquakes hit Croatia, in March with epicenter near Zagreb and December with epicenter near Petrinja, at the Faculty of Geodesy, University of Zagreb activities were initiated with the aim of strengthening the ability to react in these situations. Focus of those activities is on providing fast, adequate, and complete information on the disaster in the field of geodesy and geoinformatics. The research was focused on interpretation of kinematics of surface motion during the earthquake itself for what high rate permanent GNSS (Global Navigation Satellite System) network stations registrations are necessary. The Croatian earthquakes experience as well as the Mexico (June 2020) and Samosa earthquake (October 2020), pointed out, related to the use of high-rate registration GNSS data, that the primary problem in the use of this data is open access to the data itself. That is why this study has been launched - to gain a global picture of the availability of data from permanent GNSS networks around the world. The research included the collection and processing of information on open access policies for permanent GNSS networks data in the event of natural disasters with an emphasis on earthquakes. A global survey of institutions around the world responsible for managing GNSS permanent networks has been conducted. The survey contains three groups of questions that include general information on the type of permanent networks, models of access to network data and the readiness of countries to reach an international agreement on the opening data of the GNSS network in the event of a disaster. The results indicated that a high percentage of countries participating in the survey were ready to agree to open the data and introduce a common international portal through which scientists and researchers would be able to download GNSS permanent network data free of charge in the event of natural disasters.

Dataset and fitting methods for Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network

This upload contains raw experimental data and the fitting methods used for the article "Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network".

IWIN: The Isfjorden Weather Information Network (August 2021 - June 2023)

The Isfjorden Weather Information Network provides standard meteorological near-surface measurements from the Isfjorden region in Svalbard. The network includes weather stations permanently installed on lighthouses around the fjord and onboard small tourist cruise ships trafficking the fjord from the spring to the autumn. A defined (fixed-end) data set covering the period August 2021 - June 2023 is available here, while new observations become available in near real-time via the data portals of MET Norway (https://doi.org/10.21343/ebrw-w846; https://thredds.met.no/thredds/unis-obs/unis-obs.html).

EuroSAT Model Zoo: A Dataset of Diverse Populations of Neural Network Models - EuroSAT

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on EuroSAT. All zoos with extensive information and code can be found at <a href="http://www.modelzoos.cc">www.modelzoos.cc</a>. This repository contains two types of model populations: the base model zoo ("eurosat_cnn_kaiming_uniform.zip"), as well as a collection of sparsified model zoos (filenames ending in "magn_XX.zip" or "ard.zip"). Zoos are trained with CNN models in configurations varying the seed only (seed), and sparsification is done through magnitude-based weight pruning ("magn_XX.zip") or  varational dropout ("ard.zip"). For more information on the zoos and code to access and use the zoos, please see <a href="http://www.modelzoos.cc">www.modelzoos.cc</a>.

Network operator KPIs time series dataset

This dataset contains the measurements of different key performance indicators (KPIs) of the usage of a network operator's infrastructure. It provides time series with the evolution of the KPIs measured every 5 minutes for a time interval greather than one month. The measurements correspond to different operator locations. Four different KPIs are provided in the dataset: aggregated Internet traffic (in bits per second), downstream traffic (in bits per second), number of active client sessions, and Virtual Private Network (VPN) traffic (in bits per second). The results have been anonymized, the time frame has been shifted so that the first timestamp of each time series is 0 and the values of the KPIs have been scaled, so that they range from 0 to 1000 in each time series.

opencc-zeroJul 2023View details →

dryad40/100

Harvard Ascending Arousal Network Atlas – Version 2.0

The ascending arousal network (AAN) is a subcortical neural network that is critical to consciousness. AAN neurons connect the brainstem to the thalamus, hypothalamus, basal forebrain and cortex, activating cortically-based awareness networks. The reticular core of the AAN was first described by Moruzzi and Magoun in 1949, who coined the classical term "ascending reticular activating system" (Electroencephalogr Clin Neurophysiol 1949;1:455-73). Here, we use the term AAN because many brainstem nuclei that contribute to arousal are located outside of the pontine and midbrain reticular core (e.g., locus coeruleus, parabrachial complex, etc.), and because we believe that the word "network" appropriately connotes the physiological mechanisms by which multiple modular circuits interrelate to enable the emergent property of arousal, and hence consciousness. To date, the majority of studies investigating AAN connectivity have utilized animal models. As a result, current knowledge about the structural and functional connectivity of the human AAN is limited and largely based upon extrapolations from animal data. We created this AAN atlas to facilitate research into the structural and functional connectivity of the human AAN. The study of AAN "connectomics" has the potential to increase knowledge about arousal physiology in the human brain, as well as arousal pathology in neurological diseases, such as coma and other disorders of consciousness. In addition, the study of AAN connectomics may advance knowledge about reciprocal connectivity between this subcortical arousal network and cortically based awareness networks, such as the default mode network. In 2012, we released the Harvard AAN Atlas Version 1.0. The atlas was generated using several sources of data: 1) histologic data from a dissected ex vivo human brainstem/diencephalon specimen (Edlow et al. JNEN 2012; <a href="https://pubmed.ncbi.nlm.nih.gov/22592840/">https://pubmed.ncbi.nlm.nih.gov/22592840/</a>); 2) correlative diffusion data from the same specimen (scanned prior to serial sectioning and staining); and 3) cross-reference to the Paxinos human brainstem atlas (Paxinos G, Xu-Feng H, Sengul G, Watson C. Organization of Brainstem Nuclei, in The Human Nervous System, 3rd ed. Mai JK and Paxinos G eds. Amsterdam: Elsevier, 2011). Additional details about the development of Version 1.0 are provided at <a href="https://www.nmr.mgh.harvard.edu/resources/aan-atlas">https://www.nmr.mgh.harvard.edu/resources/aan-atlas</a>. Here, we release Version 2.0 of the atlas, which incorporates updates to AAN node neuroanatomy and nomenclature. Neuroanatomic updates are based on new immunostaining data from human brainstem specimens, which can be found at <a href="https://histopath.nmr.mgh.harvard.edu">https://histopath.nmr.mgh.harvard.edu</a>. Nomenclature updates are to ensure consistency with the Paxinos 2011 Atlas (Paxinos G, Xu-Feng H, Sengul G, Watson C. Organization of Brainstem Nuclei, in The Human Nervous System, 3rd ed. Mai JK and Paxinos G eds. Amsterdam: Elsevier, 2011). The updates in Version 2.0 are: 1) Updates to Node Nomenclature <ul> <li> mRt: cuneiform/subcuneiform nucleus (CSC) --> mesencephalic reticular formation (mRt)</li> <li> PTg: pedunculopontine nucleus (PPN) --> pedunculotegmental nucleus (PTg)</li> </ul> 2) Updates to Node Neuroanatomy <ul> <li> mRt: In Version 1.0, the CSC was localized to the rostral midbrain, based on Moruzzi and Magoun's seminal study in which stimulation of the rostral midbrain tegmentum led to arousal and cortical activation in lightly anesthetized cats. In Version 2.0, we renamed this node mRt and changed its anatomic boundaries such that it now extends throughout the entire rostro-caudal axis of the midbrain tegmentum, consistent with the Paxinos Atlas. Specifically, our mRt node now contains all of the following subnuclei described by Paxinos 2011: CnF, isRt, PrCnF, mRt, CeMe, and p1Rt. </li> <li> VTA: The VTA node in Version 2.0 has two major differences compared to the VTA ROI used in Version 1.0: 1) its lateral extent and 2) its rostral extent. In Version 1.0, we did not include the lateral wing-like extensions of the parabrachial pigmented nucleus of the VTA (PBP), as shown in Paxinos, because this subregion is difficult to differentiate from the nearby substantia nigra. However, with our new tyrosine hydroxylase immunostaining, and with reference to the Paxinos atlas, we were able to make this distinction between PBP and SN in Version 2.0. With respect to the rostral extent of the VTA node, in Version 1.0 we based the neuroanatomic borders of the VTA on hematoxylin-and-eosin-stained sections from human brainstem specimens, which revealed the highest density of catecholamine neurons, as demonstrated by the presence of neuromelanin, in the caudal midbrain. However, we recognize that VTA neurons have been identified in the rostral mesencephalon by other laboratories using a variety of staining techniques, such as tyrosine hydroxylase. Thus, we performed new tyrosine hydroxylase stains on midbrain sections from human brainstem specimens. These stains revealed a distribution of VTA neurons that was consistent with that described in prior studies by Oades, Halliday, Pearson, and colleagues. Therefore, we extended the VTA node in Version 2.0 such that it runs throughout the entire rostro-caudal axis of the midbrain. We do not include the rostral linear nucleus as part of the VTA, although some dopaminergic cells may reside in this region, because this nucleus is primarily serotonergic. Finally, we edited the midline portion of the VTA node, based on the observation that VTA neurons in the midline of the midbrain were only seen at the level of the red nucleus, not at the level of the superior cerebellar peduncle. This immunostaining observation is consistent with the VTA anatomic borders in the Paxinos atlas along the rostro-caudal axis of the midbrain. Thus, we removed the midline VTA voxels at the level of the superior cerebellar peduncle in Version 2.0. In summary, the VTA node in Version 2.0 contains the following nuclear subregions described by Paxinos: parainterfascicular nucleus (PIF); paranigral nucleus of ventral tegmental area (PN); ventral tegmental area (VTA); ventral tegmental area, rostral part (VTAR); and parabrachial pigmented nucleus of the VTA (PBP). </li> <li> PTg: Deleted one voxel in the PTg at MNI axial level z53 and reassigned this voxel to the VTA, based on tyrosine hydroxylase staining data and based on the anatomic boundaries in the Paxinos atlas.</li> <li> LDTg: Whereas LDTg was not included in Version 1.0, we have added LDTg to Version 2.0.</li> </ul>

Expanding China's protected areas network to enhance resilience of climate connectivity

The code used for analysis, mammalian species occurrence points, prediction accuracy(AUC), and prediction distribution. Specifically, The main nodes are named "network efficiency.R" and "network efficiency_random.R", which can be run in RStudio software (download at https://posit.co/), and the files "hfp_expanded.csv" and "EN_length_expanded.net" are example data. "Terrestrial mammalian species occurrence points.csv" is the latitude and longitude coordinates of occurrence records. "AUC_prediction accuracy of Maxent.xlsx" is the prediction accuracy of each species in the Maxent model. "all_species_maxent.zip" is the composite terrestrial mammalian species distribution of equal weight overlapping the spatial distribution of each species ("each_species_maxent.zip").

opencc-by-4.0May 2023View details →

Sources of prey availability data alter interpretation of outputs from prey choice null networks

Spider surveys Data collection was described previously by Cuff, Tercel, et al., (2022). This study pertains to a subset of those data, collected between 1st May and 9th July 2018 at 19 separate locations, for which paired sticky trap and vacuum sample data were collected (described below). Briefly, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from webs and the ground. Transects were randomly distributed across the entire field. Along these transects, separate 4 m2 quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active spiders were also collected. Spiders were taken to Cardiff University, transferred to fresh ethanol and stored at -80 °C in 100 % ethanol until DNA extraction. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al., (2022) and Drake et al., (2022), and is also detailed below. Extraction and high-throughput sequencing of spider gut DNA Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera Erigone, Tenuiphantes, Bathyphantes and Microlinyphia (Araneae: Linyphiidae), and the Lycosidae genus Pardosa. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017). For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively. Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet et al. (2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed Drake et al. (2022). Bioinformatic analysis The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018)  to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags. Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated. Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach Blattella germanica), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed.  The resultant sequencing read counts were converted into relative proportions (all values made to sum to one within each sample) and a mean value across the two primer pairs retained for each taxon within each sample. Relative read abundances were converted to presence-absence data of each detected prey taxon in each individual spider, but relative read abundance data were also retained for separate analyses to compare experimental outcomes between treatments. Invertebrate surveys To estimate prey availability using sticky traps, we placed one white dry 100 mm x 125 mm trap (Oecos) in the 4 m2 quadrat centred at the position where the spider was captured. The trap was suspended with wire approximately 25 mm above the ground to catch falling, crawling and flying invertebrates, and left in place for 72 hours. Invertebrates were identified on the traps under a stereomicroscope. To estimate prey availability using suction sampling, ground and crop stems were sampled using a ‘G-vac’ for approximately 30 seconds at each location. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage following suction sampling. Statistical Analysis All analyses were conducted in R v4.0.3 (R Core Team, 2021) and carried out on invertebrate data at the family or superfamily level. Alongside the dietary data derived from metabarcoding, and prey availability as determined directly by suction sampling (abundance) and sticky trapping (activity density), three additional datasets were generated where two were designed to combine data from the two trapping methods. The first approach simply set all invertebrate taxa detected in the field to have equal abundance, to provide a baseline against which to assess the effects of different prey abundance estimates. When generating the two combined data sets, it was apparent that simply adding them together would underrepresent one of the datasets as abundance and activity density are measured in different units. Therefore, a ‘proportional combined’ dataset was generated by converting counts to relative proportions of each sample (to equally weight the two methods), which were then combined by summing proportions between the two methods for each sample, multiplied by the total count of individuals across both methods for each sample (to create realistic abundance values), and then rounded to the nearest integer (to return count data). In addition, a ‘frequency of occurrence (FOO) combined’ dataset was generated by converting counts to binary presence-absence values of each sample, which were then summed between the two methods for each sample. To assess the diversity represented by the two sampling methods and their combinations, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell et al., 2021) using the ‘iNEXT’ package with families represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016). The remaining analyses were performed using both presence-absence and relative read abundance dietary data separately to show how differences in the treatment of the observed data are reflected in the outcomes of the analyses. Figures and outputs given in the main text relate to the presence-absence data, while relative read abundance figures and outputs are presented in the Supplementary Information. Prey preferences of spiders were analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ function. Econullnetr generates null models based on prey availability to predict how consumers would forage if based on the availability of resources alone. These null models are then compared against the observed interactions of consumers (e.g., interactions of spiders with their prey based on dietary metabarcoding) to ascertain the extent to which resource consumption deviated from random. In five separate null models, prey availability was represented separately by the datasets described above: abundance (suction sampling), activity density (sticky trapping), proportional combined, FOO combined and equal prey abundance. To compare effect sizes between null models for each resource taxon, mean prey preference standardised effect size (SES) values were calculated from the individual spiders per model. The SES values were plotted and joined between taxa to visualise paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via an econullnetr null model with 999 simulations with outputs extended to allow the comparison of the null interactions for individual consumers (generate_null_net_indiv; Cuff, Kitson, et al., 2023). A visualisation of the per-individual differences in null model and observed data was generated via non-metric multi-dimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 9999 simulations, with Euclidean distance. Centroid coordinates for each null model and the observed data were extracted and pairwise distances calculated between model centroids: The ‘observed’ network (i.e., the network determined solely by dietary data, not necessarily the objectively ‘true’ network) and each null network were visualised with the associated prey choice effect sizes as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016) via an ‘igraph’ object (Csardi & Nepusz, 2006). The degree of each prey node, weighted nestedness and linkage density were generated using the ‘bipartite’ package (Dormann et al., 2008) for each network and compared visually via ggplot2.

Similarity network fusion to identify phenotypes of small for gestational age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multi-omic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

opencc-by-4.0Dec 2022View details →

Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model

Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features learned by these models are still poorly understood and not easy to interpret. We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors. The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model outperforms state-of-the-art visible machine learning models.

opencc-by-3.0-usJul 2023View details →

CePNEM model analysis data and ANTSUN and microscopy neural network weights

Citation and publication To cite this work or access the publication, please use the citation information listed here: <a href="https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation">https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation</a>   Initially published as preprint in: Brain-wide representations of behavior spanning multiple timescales and states in C. elegans Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell bioRxiv 2022.11.11.516186; doi: <a href="https://doi.org/10.1101/2022.11.11.516186">https://doi.org/10.1101/2022.11.11.516186</a> * Equal Contribution   Contents 1. deepnet-weights.tar.bz2 contains the trained weights of the neural networks used in this project. 3dunet_540nm_voxels: 3D U-Net for segmenting neurons head_detector_unet: finding worm head landmark used in ANTSUN registration head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets microscope_tracker: detecting keypoints for online tracking on the microscope behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification 2. data files ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (<a href="https://github.com/flavell-lab/AtanasKim-Cell2023/">https://github.com/flavell-lab/AtanasKim-Cell2023/</a>) on using these datasets.

Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks

This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, currently under review.  The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis. The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.

Supplementary dataset for "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking"

The supplementary dataset for the paper "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking". We include the splits for cora, citeseer, and pubmed, the hard negative samples, and the node2vec embeddings. We also include a jupyter file read_data.ipynb to show how to read the non-txt file. <ul> <li>heart_test_samples.npy, heart_valid_samples.npy: the heard negative samples</li> <li>*-n2v-embedding.pt: node2vec embeddings</li> <li>test_samples_index.pt, valid_samples_index.pt: the node index of the selected samples in ogbl-ppa under HeaRT</li> <li>gnn_feature: the input feature of cora, citeseer, pubmed</li> </ul> More details for our code and how to use the dataset are on the code repository: https://github.com/Juanhui28/HeaRT .

opencc-by-4.0Dec 2021View details →

A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements

Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks. If you use our dataset in your research, we kindly request that you cite the following paper: K. Kousias et al., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in IEEE Communications Magazine, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.

opencc-by-4.0Jan 2021View details →

Global health science leverages established collaboration network to fight COVID-19

Compressed file containing the data for the <a href="https://arxiv.org/abs/2102.00298">Global health science leverages established collaboration network to fight COVID-19</a> paper. For simplicity of use you can find pubmed__2019_cleaned.rar at https://zenodo.org/record/5011448#.Ykb2PDU68mA which contains the cleaned data used for the analysis. However we do not own the data and The following 6 data sources have been used. 1. Publication data source: PubMed API  (download 26.06.2023). Output: country_pub_info.csv and edge_list.csv 2. Covid cases (download 26.06.2023) file: owid-covid-data.csv website: https://github.com/owid/covid-19-data/tree/master/public/data original source (for some variables): COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University 3. Covid policy restrictions (last download 21.06.2021) website: https://ourworldindata.org/policy-responses-covid data file: OxCGRT_latest.csv Oxford Covid-19 Government Response Tracker cit: Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).” Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8   4. Economic wealth Penn World Table version 10.0 cit: Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt 5. Economic/social development Human Development Index (HDI) from http://hdr.undp.org/en/content/download-data

Synthetic gene expression data with underlying gene network

This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest. In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023). The data is generated by the function gen_data in R package networkRF which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the num.var argument of the function gen_data. More descriptions on the implementation and the format of the output can be found in the help page of the R package.

opencc-zeroAug 2023View details →

dryad40/100

Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)

Described in 2009, the Patch-nosed Salamander (Urspelerpes brucei) is a miniature species of lungless salamander with a geographic range of only ~45 km2. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of U. brucei and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of U. brucei, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which U. brucei lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks" URL: https://github.com/qianghuangwhu/benchtemp Openreview: https://openreview.net/forum?id=rnZm2vQq31 There are 19 (15+4)  benchmark temporal graph datasets: reddit, wikipedia, mooc, lastfm, enron, SocialEvo, uci, CollegeMsg, TaobaoSmall, CanParl, Contacts, Flights, UNtrade, USLegis, UNvote, DGraphFin, TaobaoLarge, YoutubeReddit, YoutubeRedditLarge   Each dataset has three files: 1. ml_{data_name}.csv - the csv file of the Temporal Graph. This file have five columns with properties: 'u': the id of the user. 'i': the id of the item. 'ts': the timestamp of the interaction (edge) between the user and the item. 'label': the label of the interaction (edge). 'idx': the index of the interaction (edge). For example: ,u,i,ts,label,idx 0,1,2,0.0,0.0,1 1,1,3,0.0,0.0,2 2,1,4,0.0,0.0,3 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph.. 3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.