Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)

Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study  (80 ASD and 80 TD, 80 Training set and 80 Testing set). With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.

opencc-by-4.0Jan 2024View details →

Output Dataset from "Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks"

Output dataset of stellar parameters and associated uncertainties derived by the machine learning approach, as decribed in <a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">"Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks", Fallows & Sanders (</a><a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">2024)</a>.  Included parameters: [Fe/H] (fe_h), effective temperature (teff), surface gravity (logg), [C/Fe] (c_fe), [N/Fe] (n_fe), and [a/M] (a_m). Gaia DR3 source ids are included for matching purposes, alongside Gaia bp_rp_excess_factor and ruwe metrics for quality filtering. We include predictions for only Gaia objects with radial velocity measurements ('output_0.0_360.0_v2.txt'), and for all* Gaia objects with XP spectra ('gaiaFull_output_0.0_360.0.zip'). Note our full catalogue has a total size of 30.9GB once uncompressed.   * Not truly all objects with XP spectra; stars with spurious or unreliable measurements in our requred inputs (Gaia, 2MASS, WISE) have been removed.

dryad40/100

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Information processing and communication in neuronal circuits is enabled by dynamic networks of inter-areal coupling of neuronal oscillations in which hubs play a central role for regulation of communication. Oscillations are shaped by interactions between pyramidal cells and interneurons and are locally influenced by neuromodulatory systems. Here, we set out to investigate how sparial variability in neurotransmitter receptor and transporter density influences frequency-specific large-scale networks of phase-synchrony (PS) and amplitude-correlation (AC) in human magnetoencephalography data. We found that node centrality - indexing which individual brain regions function as hubs - covaried positively with GABA, NMDA, dopaminergic, and most serotonergic receptor and transporter densities in lower frequency bands (delta to low-alpha for PS, and delta for AC) and in the gamma band, but negatively in between. These results establish how local microarchitecture influences large-scale connectivity networks of neuronal oscillations in the human brain in frequency- and spatially-specific patterns.

opencc-zeroJul 2024View details →

opencc-by-4.0Jul 2024View details →

Decentralized Applications Network Structure

This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore. Each dApp's network structure is represented through a CSV which includes the following information: File: The name of the Solidity file Source_Contract: The name of the contract that calls the Target contract. Target_Contract: The name of the contract that is called by the Source contract. Source_Function: The name of the function that calls the Target function. Target_Function: The name of the function called by the Source function. Chain: The chain of function calls.

dryad40/100

BOLD5000 Additional ROIs and RDMs for neural network research

Artificial neural networks (ANNs) are sensitive to perturbations and adversarial attacks. One hypothesized solution to adversarial robustness is to align manifolds in the embedded space of neural networks with biologically grounded manifolds. Recent state-of-the-art works that emphasize learning robust neural representations, rather than optimizing for a specific target task like classification, support the idea that researchers should investigate this hypothesis. While works have shown that fine-tuning ANNs to coincide with biological vision does increase robustness to both perturbations and adversarial attacks, these works have relied on proprietary datasets- the lack of publicly available biological benchmarks make it difficult to evaluate the efficacy of these claims. Here, we deliver a curated dataset consisting of biological representations of images taken from two commonly used computer vision datasets, ImageNet and COCO, that can be easily integrated into model training and evaluation. Specifically, we take a large functional magnetic resonance imaging (fMRI) dataset (BOLD5000), preprocess it into representational dissimilarity matrices (RDMs), and establish an infrastructure that anyone can use to train models with biologically grounded representations. Using this infrastructure, we investigate the representations of several popular neural networks and find that as networks have been optimized for tasks, their correspondence with biological fidelity has decreased. Additionally, we use a previously unexplored graph-based technique, Fiedler partitioning, to showcase the viability of the biological data, and the potential to extend these analyses by extending RDMs into Laplacian matrices. Overall, our findings demonstrate the potential of utilizing our new biological benchmark to effectively enhance the robustness of models.

opencc-zeroJun 2024View details →

opencc-by-4.0Jul 2024View details →

Extinction Rebellion Finland (Elokapina) Post-Repression Twitter Activity and Network Patterns

Overview This repository contains two time series data sets of activity levels and network patterns on Finnish climate Twitter for the period of July 18, 2020 to February 11, 2022. First, there is a day-level data set (574 days) containing Twitter activity levels of climate activists and non-activists. Second, there is a week-level data set (81 weeks) containing estimated coefficients for various network effects from exponential random graph models (ERGM) fit to retweet networks. For details, including definitions of activist/non-activist and the ERGM specifications, please see the referenced work: "Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism", detailed below.   Data Set Details `raw_daily_counts.csv` contains the following variables at the day level. <ul> <li>date: from 2020-07-25 to 2022-02-11</li> <li>activity_activist: the daily activity count of activist users on Finnish climate Twitter</li> <li>activity_nonactivist: the daily activity count of non-activist users on Finnish climate Twitter</li> <li>users_activist: the daily count of activist users on Finnish climate Twitter</li> <li>users_nonactivist: the daily count of non-activist users on Finnish climate Twitter</li> </ul> `weekly_ERGM_coefficients.csv` contains a subset of estimated coefficients from ERGMs of weekly retweet networks from Finnish climate Twitter. The data set contains the following variables (coefficients), not all of which are from the same model specification. <ul> <li>week: starting with week 2 (2020-07-25 to 2020-07-31) and ending with week 82 (2022-02-05 to 2022-02-11)</li> <li>political_activity_activist: activists retweeting politically relevant users</li> <li>political_activity_nonactivist: non-activists retweeting politically relevant users</li> <li>gwidegree0_activist: geometrically weighted in-degree (decay = 0) for the activist subnetwork</li> <li>gwidegree0_nonactivist: geometrically weighted in-degree (decay = 0) for the non-activist subnetwork</li> <li>gwidegree0_activistsub_thresh3: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh4: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh5: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh6: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh7: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh8: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li>gwidegree0_activistsub_thresh9: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> </ul>   References Please reference the original study when using this data set. Savolainen, Sonja, Ville P. Saarinen, and Ted Hsuan Yun Chen. 2024. “Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism.” <a href="https://doi.org/10.31235/osf.io/p4yvk" target="_blank" rel="noopener">doi:10.31235/osf.io/p4yvk</a>.

Fig. 3. Minimum spanning network for Haemoproteus and Plasmodium mitochondrial DNA cytochrome b in Spatial, temporal, molecular, and intraspecific differences of haemoparasite infection and relevant selected physiological parameters of wild birds in Georgia, USA

Fig. 3. Minimum spanning network for Haemoproteus and Plasmodium mitochondrial DNA cytochrome b haplotypes detected in four species of passerines from Georgia (USA). Circles are drawn proportional to the frequency at which haplotypes were observed. Color represents the host species from which haplotypes originated: red for Northern Cardinal (Cardinalis cardinalis), blue for Indigo Bunting (Passerina cyanea), yellow for White-throated Sparrow (Zonotrichia albicollis), and grey for Tufted Titmouse (Baeolophus bicolor). A single mutation separates nodes unless explicitly indicated by number. Letters within each node refer to Table 8 which indicates the haplotype name, sampling location, and other factors associated with hosts.

Fig. 1 in Testing the robustness of transmission network models to predict ectoparasite loads. One lizard, two ticks and four years

Fig. 1. Transmission networks generated with (a) a short time window of infection; and (b) a long time window of infection, from the GPS location data of the lizards in the study population in 2010. Nodes represent individual lizards and edges between nodes are directed towards the lizard that is at risk of infection. The edges are weighted as described in the main text and the thicker the line the more weight is associated with that edge.

Fig. 4. Minimum spanning network for hematozoa mitochondrial DNA cytochrome b in Evaluation of blood and muscle tissues for molecular detection and characterization of hematozoa infections in northern pintails (Anas acuta) wintering in California

Fig. 4. Minimum spanning network for hematozoa mitochondrial DNA cytochrome b haplotypes detected in Central Valley northern pintails. Circles are drawn proportional to the frequency at which haplotypes were observed. Shading represents the sample collection from which haplotypes originated: white (2006– 2007 wing muscle), grey (2011–2012 wing muscle), and black (2011–2012 blood). A single mutation separates nodes unless explicitly indicated by number. Lines separating nodes are drawn to scale unless indicated by a break. Parasite taxa have been abbreviated in haplotype names (Leu = Leucocytozoon, Hae = Haemoproteus and Pla = Plasmodium).

Fig. 2 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology

Fig. 2. How do we use networks to understand the ecology of parasite transmission? Networks allow us to describe how the behaviour of individuals collectively affects the transmission of parasites within wildlife populations. They provide a flexible framework that enables analysis at three different levels; individual (panel A), dyadic (pair-wise associations) (panel B) and the network (population) level (panel C). Within each level of analysis, there are different metrics and analytical approaches that can be used to explore the ecology of parasite transmission.

Fig. 1 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology

Fig. 1. What is a network? A network in its most elementary form is an adjacency matrix, where row and column labels represent the individuals in the network, and the remaining cells represent the pair-wise associations among individuals in the network (panel A). These associations can be weighted, as below (panel A), where stronger relationships are assigned a higher value (for example, the duration or frequency of contact). They can also be directed, to reflect the direction of the association; in this instance, the direction of possible parasite transmission. In this case, rows represent donor nodes, and columns represent recipient nodes (e.g., in panel A: from node C (donor) to node D (recipient), there is a score of 1). The matrix can be visualised as a network diagram (panel B), consisting of nodes, which represent the epidemiological unit of interest (usually individuals) connected together by a series of edges representing the measure of association (the potential for parasite transmission). In context of understanding the ecology of parasite transmission, edges represent a 'contact' between two hosts that provides an opportunity for parasite transfer. The weighting of edges represents the likelihood of parasite transmission (e.g., the frequency or intensity of contact among hosts). The definition of a contact will depend on the type of parasite considered, and how it is passed from one host to another.

Fig. 4 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 4. Results of the Geneland analysis. A: Bar plot of posterior probability density according to the number of clusters; B: posterior probability maps for the delimited clusters.

Fig. 3 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 3. Pairwise FST between localities of Telmatobius pefauri obtained using mitochondrial (A) and microsatellite (B) data. The colour scale corresponding to the values of FST is shown to the right of each matrix. Significant (Bonferroni corrected) comparisons showing p <0.05, p <0.01 and p <0.001 are denoted by *, ** and ***, respectively.

Fig. 2 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 2. Median-joining network based on the fragment of the analysed control region. Table 1. Indices of mitochondrial diversity, nuclear diversity, and inbreeding coefficients (FIS) by locality

Fig. 1 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 1. Study area, distribution of Telmatobius pefauri. Localities, 1: Socoroma (Socoroma River); 2: Murmuntani; 3: Copaquilla; 4: Chapiquiña; 5: Belén; 6: Lupica; 7: Saxamar. Localities 2 and 3 belong to the Seco River drainage; localities 4–7 belong to the Tignamar River drainage. Basin limits are indicated with dashed lines. The inset map shows the study area (highlighted by a red box) in relation to South America. SAAD = South American Arid Diagonal.

Fig. 5 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 5. Scatter plot for the first two principal components obtained in the Principal Components Analysis using SSR data.

opencc-by-4.0Apr 2023View details →

Fig. 3. Haplotype networks for A. oahuensis. Panels correspond with the 12S in Tuerkayana latens Ng and Hsi-Te Shih 2023, n. sp.

Fig. 3. Haplotype networks for A. oahuensis. Panels correspond with the 12S rDNA gene (Panel A), 16S rDNA gene (Panel B), COI (Panel C), and H3A (Panel D). Colors and locality IDs correspond with those used in all other Figures. Black circles represent unsampled (i.e., missing) haplotypes, with the size of circles proportional to the frequency at which each haplotype was recovered.

opencc-by-4.0Nov 2022View details →

Fig. 3. Genealogical network for the 557 in Fig. 5 in Fig. 1 in Fig. 20. A-E in Establishment of A New Bornean Genus of Gecarcinucidae (Crustacea: Brachyura), with Descriptions of Five New Species.

Fig. 3. Genealogical network for the 557-bp 16S haplotypes observed within Tubuca arcuata, constructed with TCS. The ancestral haplotype is indicated by square. Green and red circles mean the haplotypes of the N and W clades, respectively; and the square and circle in purple represent the haplotypes that are shared by N and W clades. Unlabelled hatches and node indicate inferred haplotypes not found in the sampled population.

opencc-by-4.0Nov 2022View details →

Fig. 4. Genealogical network for the 658 in Fig. 5 in Fig. 1 in Fig. 20. A-E in Establishment of A New Bornean Genus of Gecarcinucidae (Crustacea: Brachyura), with Descriptions of Five New Species.

Fig. 4. Genealogical network for the 658-bp COI haplotypes observed within Tubuca arcuata, constructed with TCS. The ancestral haplotypes of the N and W clades are indicated by squares. Unlabelled hatches and nodes indicate inferred haplotypes not found in the sampled population.