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opencc-by-4.0Feb 2023View details →

CryptoSentiment: A large scale sentiment dataset for cryptocurrencies

CryptoSentiment is a  dataset, which contains sentiment information about cryptocurrency assets, gathered by various online sources, and analyzed by FinBERT sentiment extractor. More specifically, we provide a publicly available dataset containing fine-grained sentiment analysis data (minute-basis) about cryptocurrency market collected by different online sources. CryptoSentiment dataset includes 235,907 sentiment scores for 14 different cryptocurrencies gathered from various online sources such as news articles and social media.

opencc-by-4.0Mar 2023View details →

Dataset to Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution

Six sensitivity experiments were conducted with the ocean-sea ice-biochemistry model FESOM2.1-REcoM3 to investigate the sensitivity of total alkalinity to changes in parametrizations. The results are reported in part two of the report: Hinrichs, C., & Hauck, J. (2022). OceanNETS Task 4.4 – Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. <a href="https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf">https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf</a> This item contains the data and plotting routine to reproduce Figure 14 in the report. The model data has been post-processed into global mean depth profiles.

opencc-by-3.0May 2022View details →

Gollum: A Gold Standard for Large Scale\\Multi Source Knowledge Graph Matching

The set of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing. For an integrated view and usage, an alignment between these KGs is necessary on the schema as well as instance level. There are already approaches which try to tackle this multi source knowledge graph matching problem, but large gold standards are missing to evaluate their effectiveness and scalability. In particular, most existing gold standards are fairly small and can be solved by matchers which match exactly two KGs (1:1), which are the majority of existing matching systems. We close this gap by presenting Gollum -- a gold standard for large-scale multi source knowledge graph matching with over 275,000 correspondences between 4,149 different KGs. They originate from knowledge graphs derived by applying the DBpedia extraction framework to a large wiki farm. Three variations of the gold standard are made available: (1) a version with all correspondences for evaluating unsupervised matching approaches, and two versions for evaluating supervised matching: (2) one where each KG is contained both in the train and test set, and (3) one where each KG is exclusively contained in the train or the test set. We plan to extend our KG track at the Ontology Alignment Evaluation Initiative (OAEI) to allow for matching systems  which are specifically designed to solve the multi KG matching problem. As a first step towards this direction, we evaluate multi source matching approaches which reuse two-KG (1:1) matchers from the past OAEI.   Due to the size of the KG files, they are hosted at the institute: <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar</a>    (50,3 GB) <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar</a>      (74,7 GB) <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar</a>   (25,3 GB)

opencc-by-4.0Nov 2022View details →

Maxima of Station-based Rainfall Data over Different Accumulation Durations and Large Scale Covariates

Description These data were used in the study "Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe" (Fauer et al., 2022, https://doi.org/10.21203/rs.3.rs-2542862/v1). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual and monthly precipitation maxima over different durations are published here. For more detailed information on our work and the modeling of extreme rainfall data, see also Fauer et al. (2021, https://doi.org/10.5194/hess-25-6479-2021).   Files <ul> <li>precipMax_and_covariates.csv: This file contains aggregated rainfall data over different durations and for different stations ("xdat"). Also, it contains covariates for the variables "blocking", "NAO", surface air temperature ("tas"), humidty and their polynomials up the the fourth order.</li> <li>precip_meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station. The value in "group" corresponds to the value "station" in precipMax_and_covariates.csv.</li> </ul> Abstract of the according study Extreme precipitation shows non-stationary behavior over time, but also with respect to other large-scale variables. While this effect is often neglected, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. The models are selected with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large scale information. Also, the new model enables insights into the effect of large scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.   Acknowledgements We would like to thank the German Weather Service (DWD), especially Thomas Junghänel, and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.   Funding This study is part of the ClimXtreme project (Grant number 01LP1902H) and is sponsored by the Federal Ministry of Education and Research in Germany.   Changelog <ul> <li>added humidity as large-scale covariate</li> <li>large-scale covariates are normalized to mean=0 and standard deviation=1</li> </ul>

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Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information

Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study.

opencc-zeroApr 2023View details →

Data from: A framework to apply trait-based ecological restoration at large scales

R scripts and data to run the proposed framework at Coutinho, A. G., Carlucci, M. B., Cianciaruso, M. V. (2023) A framework to apply trait-based ecological restoration at large scales. Journal of Applied Ecology.

Large-scale Docking Datasets for Machine Learning

Large-scale virtual screening has become a valuable tool for early-phase drug discovery. Recent expansions of commercial chemical space have made it computationally intractable to evaluate all compounds in the libraries. Machine learning is one of the methods that aim to prioritize specific subsets of these vast libraries. In order to put these methods to the test, access to large-scale datasets is beneficial. To help the community benchmark their work, we share the docking scores of several ultralarge virtual screening campaigns. The datasets we provide contain canonical SMILES, compound identifiers, and docking scores. We docked two different chemical libraries against eight different biological targets with therapeutic relevance. The first dataset contained approximately 15.5 million molecules adhering to the "Rule-of-Four", whereas the second datasets consists of approximately 235 million "lead-like" molecules. The biological targets represent different classes of proteins and binding sites. More details on the datasets and our methods can be found on (<a href="http://github.com/carlssonlab/conformalpredictor">https://github.com/carlssonlab/conformalpredictor</a>) and our pre-print (<a href="https://doi.org/10.26434/chemrxiv-2023-w3x36">https://doi.org/10.26434/chemrxiv-2023-w3x36</a>). Please feel free to download and use these datasets for your own research purposes. We only ask that you cite our pre-print and datasets appropriately if you use it in your work. Thank you for your interest in our research!

Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data

This is the data required to reproduce the results of the manuscript "Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data", including measurements of tree canopy cover extracted from the Global Forest Cover Change dataset (Townshend 2016).   Reference Gillard, G. L. & Rowley, J. J. L. (2023). Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data. Journal of Zoology. [In publication].   Global Forest Cover Change Dataset Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.

Long-term spatial memory, across large spatial scales, in Heliconius butterflies

Data accompanying "Long-term spatial memory, across large spatial scales, in Heliconius butterflies", Current Biology 2023:   exp1.csv. Behavioural data from experiment 1. exp2.csv. Behavioural data from experiment 2. exp3.csv. Behavioural data from experiment 3. Exp1&2.csv. Behavioural data comparing experiment 1 and 2. Exp1byDay.csv. Behavioural data for experiment 1 split by day. Exp2byDay.csv. Behavioural data for experiment 2 split by day. Exp3byDay.csv. Behavioural data for experiment 3 split by day. exp1.R. R code for experiment 1 analysis. exp2.R. R code for experiment 2 analysis. exp3.R. R code for experiment 3 analysis. exp1vsExp2.R. R code for comparing experiment 1 and 2.

INCYDE: A large scale cyclone detection and intensity estimation dataset using satellite infrared imagery

INCYDE (INSAT-based Cyclone Detection and Intensity Estimation) is a cyclone detection and intensity estimation dataset. The cyclone images in the dataset are captured from INSAT 3D/3DR satellites over the Indian Ocean. The proposed INCYDE dataset contains over 100k cyclone images with augmentations taken from cyclones over the Indian Ocean from the year 2013 to 2021. The dataset pertains to two specific tasks: cyclone detection as an object detection task, and intensity estimation as a regression task. In addition to the dataset, this study introduces baseline models that were trained on the newly presented dataset

PubGraph: A Large-Scale Scientific Knowledge Graph

We present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at <a href="https://purl.archive.org/pubgraph">https://purl.archive.org/pubgraph</a> and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.

Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation

Biophysical effects of different bioenergy crop cultivation scenarios on global water cycles simulated by coupled IPSL-CM model, with ORCHIDEE-MICT-BIOENERGY as the land component and LMDz as the atmosphere component. The spatial resolution of the coupled model was 1.26° latitude × 2.5° longitude (i.e., 143*144 grid cells globally). Datasets includes: 1. Source data and plotting code for Figure 1, the global precipitation changes induced by bioenergy cultivation. 2. Source data and plotting code for Figure 2, the diagnostic precipitation changes for global land area, in and outside of the bioenergy cultivation area. 3. Source data plotting code for Figure 3, the changes in water balance for global land area, in and outside of bioenergy cultivation area, monsoon regions and four different humidity zones.

Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data

These are the data and resources used for a Twitch Emote recommendation system using a Word2Vec model. The nature and exploration of the data is described in Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data. To protect the privacy of the users whose messages were scraped to build this corpus, names and timestamps have been removed and only the message bodies are included. However, a tutorial for this project is included on the project GitHub: https://github.com/KoroshM/Emote-Recommender. embeddings.tsv and labeled_metadata.tsv may be used in TensorFlow's embedding projector to visualize the embedding space. Note: Model files are the following: embeddings.tsv labeled_metadata.tsv model model.model** model.wv.vectors.npy **Located here: https://drive.google.com/drive/folders/1RZC4JA4CpAcwoo6dOwq_jobTd6dNi_n2?usp=sharing

Figure S2 in Large-scale snake genome analyses provide insights into vertebrate development

Figure S2. Snake genome features, related to Figure 2 (A) Evolution of chromosomes in snakes. In total, 23 proto-chromosomes of Serpentes were reconstructed using four lizards as outgroup. (B) Circos plots showing conserved synteny between the hypothesized Serpentes ancestor: Serpentes (red) and Hong Kong dwarf snake (Csep-blue). (C) Snake body lengths were significantly negatively correlated with the genome evolutionary rate (correlation coefficient = 0.50, p value = 0.011). (D) Length distribution of snake ancestor gain and lost genome segments. Length distribution of snake ancestor unique genome segments (left). Length distribution of snake ancestor lost genome segments (right).

Figure S3 in Large-scale snake genome analyses provide insights into vertebrate development

Figure S3. Snake-specific genome structural variations (SSSVs), snake-diverged conserved non-coding elements (SD-CNEs that were diverged in snakes but were still conserved in the outgroup) (SD-CNEs), and orthologous genes used for evolutionary analysis, related to Figure 3 and STAR Methods (A) SSSVs distribution in different genome regions. (B) Genomic reads coverage of the snake-specific lost gene GHRL across four lizards (green anole [Acar], Anan's rock agama [Lsac], Komodo dragon [Vkom],and viviparous lizard [Zviv]), and 14 newly sequenced snakes. (C) Z-score cut off of SD-CNEs. (D) The enriched MGI terms that related to snake phenotypes for genes with SD-CNE (adjusted p value <0.05). SD-CNEs (dots) are located around the transcription start site of the genes enriched in eye, eyelid, ear, lung, mandible, maxillary, sternum, and tooth development-related terms. (E) Enriched GO terms (adjusted p value <0.05) for the newly evolved coding genes in snakes. Those related to dietary excess, protein digestion, and olfactory receptor activity were colored in red, blue, and brown, respectively. (F) Enriched GO terms (adjusted p value <0.05) for snake-specific coding genes that were lost. Red, blue, orange, and green were used to indicate the terms involved in vision, lens development, appetite, and bile acid biosynthetic process, respectively. (G) SSSV deletion in the potential regulatory region of RP1. (H) Counts of orthologous genes used for evolutionary analysis among 27 selected species.

Figure 3 in Large-scale snake genome analyses provide insights into vertebrate development

Figure 3. Genetic basis of skeletal system evolution and organ adaptation in snakes PSGs, REGs, WGCNA hub genes, lost genes, SSSV-related genes, newly evolved genes, and SD-CNE-associated genes are marked in different colors and are represented by rectangles. (A) Renewal of genomic elements contributing to snake skull development and digestion. Related genes are shown in their corresponding regions. (B) The evolution of genomic elements has potentially facilitated the evolution of the elongated body plan of snakes. (C) The evolution of genes related to lung development and function. The heatmap shows the expression levels of WGCNA hub genes, PSGs, and SSSV-related genes in green anole (Acar), keeled slug snake (Pber), and many-banded krait (Bmul) (The ''_numbers'' represents the different copies of a multi-copy gene). (D) Whole-body X-ray images and relative lengths of the fourth forelimb phalanx and third hindlimb phalanx of wild and PTCH1-mutated mice (eight samples per group, the significance is indicated as *p <0.05). Mean ± SD is shown by error bar. See also Figure S4 and Table S3.

Figure S1 in Large-scale snake genome analyses provide insights into vertebrate development

Figure S1. Phylogenetic and divergent time tree of snakes, related to Figure 1 (A) Phylogenetic tree distributions of selected snake species, topology inferred from a previous study.173 (B) Maximum likelihood (ML) phylogenetic tree inferred from the 31-taxon whole-genome alignments. Ultrafast bootstraps were repeated 3,000 times, with NNI UFBoot tree optimization and SH-like approximate likelihood ratio test (SH-aLRT) performed. SH-aLRT support rate/ultrafast bootstrap support rate is indicated at each node. (C) Inferred ML phylogenetic tree using orthologous genes. 1,980 1:1 orthologous genes were concatenated to a super gene sequence for constructing the ML phylogenetic tree using IQ-TREE. 10,000 ultrafast bootstraps were carried out and the two support rates were marked at each node. (D) ML phylogenetic tree inferred from 4d sites. 4d sites were extracted from the orthologous genes and taken as input for IQ-TREE to infer the ML phylogenetic tree. The two supports were computed by 10,000 ultrafast bootstraps and SH-aLRT. (E) Inferred ML phylogenetic tree of conserved non-coding elements (CNEs). All CNEs were identified and concatenated into a single sequence for the ML phylogenetic tree inferred using 5,000 ultrafast bootstraps and SH-aLRT. (F) Coalescent phylogenetic tree inferred from 51,302 1-kb orthologous genomic segments using ASTRAL-III. (G) Coalescent phylogenetic tree inferred from 1,980 1:1 orthologous genes using ASTRAL-III. The poorly supported nodes are indicated in red. (H) DicoVista gene tree topologies frequency analysis. The frequency of three topologies (t1–t3) is shown, and the red is the main topology. The divergence time (million years ago [mya]) of the species is estimated using r8s and the incongruent clades are in red. (I) Divergence time of the 31 species. r8s estimated divergence time using the whole-genome alignments.The estimated divergence time (million years ago [mya]) is labeled on each node and 6 calibrating nodes used are marked. (J) MCMCTree estimated divergence time of the 31 species using 4d sites. Six calibrating nodes used are marked and the estimated divergence time represented in million years is labeled.

Figure 5 in Large-scale snake genome analyses provide insights into vertebrate development

Figure 5. Evolution of blind and infrared-sensitive snakes (A) Specialized traits of blind snakes. (B) Gene regression underlies eye degeneration in blind snakes. Lost genes are shown in green. Heatmap showing the significant down-regulation of the expression of genes in blind snake eyes (QN, quantile normalized). (C) BSD-CNE-associated genes enriched in odontogenesis and bone-development-related GO terms. (D) Amino acids sequences alignment (left) and enzymatic activity (right) of CHIA. The amino acids in red are positively selected sites in blind snakes. The error bar represents mean ± SD of enzymatic activity. The enzymatic activity is significantly higher in Diard's blind snake (Student's t-test p value = 8.78e-09). (E) Infrared sensing-related genes in infrared-sensitive snakes. PSGs, REGs, and variation-shared genes are indicated in purple, blue, and red, respectively. Average expression levels (represented by FPKM) of these genes in seven tissues of the brown-spotted pitviper are shown. See also Figure S5 and Tables S4 and S5.

Figure 4 in Large-scale snake genome analyses provide insights into vertebrate development

Figure 4. Genome features associated with snake-specific sense organ evolution, highlighting PSGs, REGs, newly evolved genes, SD-CNEassociated genes, lost genes, and SSSV-associated genes (A) Lost and highly expressed genes in snake eyes. Diagram of the snake eye showing lost genes in blue. Heatmap of highly expressed genes (QN, quantile normalized). (B) Diagram of the snake inner ear. Twelve SD-CNE-associated genes and four SSSV-associated genes are involved in ear development. Expression levels (scaled fragments per kilobase per million mapped reads [FPKM]) of sound perception-related PSGs and REGs in 10 keeled slug snake (Pber) tissues are shown in circles. Circle size is positively correlated to the expression level. (C) Diagram of the taste transduction process. Solid lines indicate direct interactions, and dotted lines indicate indirect interactions. See also Table S3.

Figure 1 in Large-scale snake genome analyses provide insights into vertebrate development

Figure 1. Phylogeny of snakes Maximum-likelihood phylogenetic tree inferred from whole-genome sequences of 31 species. Divergence times of all nodes were estimated by r8s with whole-genome sequences using six calibration points (Figures S1I and S1J). All genomes generated in this study are in red. Maps were taken from those in a previous study.24