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161 results for “graph dataset”
The WASABI Dataset and RDF Knowledge Graph
<p>The WASABI Dataset and RDF Knowledge Graph is rich dataset describing more than 2 millions commercial songs, 200K albums and 77K artists (mainly from pop/rock culture). It comprises data extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis.</p> <p>This is version 2 of the dataset. It consists of two representation formats:</p> <ul> <li>The JSON format provides all data extracted from the MongoDB database that backs up the web application</li> <li>The RDF Knowledge Graph that represents the same data following the WASABI ontology.</li> </ul> <p>WASABI project homepage: http://wasabihome.i3s.unice.fr/</p> <p>Github: https://github.com/micbuffa/WasabiDataset</p>
Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks - Dataset
<p>This is the dataset of "Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks"</p>
Dataset for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"
<p>Datasets for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"</p> <p>The code repository is <a href="https://github.com/THUDM/SelfKG">here</a>, and our paper is <a href="https://arxiv.org/abs/2203.01044">here</a>.</p>
Wikipedia Knowledge Graph dataset
<p>Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.</p> <p>There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the <em><strong>page</strong></em> file, after it there are 4 files with different entities related to the Wikipedia pages (<em><strong>category</strong></em>, <em><strong>url</strong></em>, <em><strong>pub</strong></em> and <em><strong>page_property</strong></em> files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (<em><strong>page_category</strong></em>, <em><strong>page_url</strong></em>, <em><strong>page_pub</strong></em> and <em><strong>page_link</strong></em> files).</p> <p>The document <em><strong>Dataset_summary</strong></em> includes a detailed description of the dataset.</p> <p>Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.</p>
Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Full Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Dataset and Model Weights for Plasma Sheet Model Graph Network Simulator
<p>This repository contains the simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].</p> <p>Two *.zip files are provided:</p> <ul> <li>data.zip - contains the datasets of train/test simulations produced using the Sheet Model algorithm [1, 2]</li> <li>models.zip - contains the GNN model weights (<em>*.</em>pkl<em>) </em>+ relevant training information and model parameters <em>(*.</em>yml<em> and *</em>.txt)</li> </ul> <p>Dataset subfolders are named according to dataset/{'train' or 'test'}/{number of sheets}/{boundary condition}/. Each subfolder contains multiple simulations and a single info.yml file with relevant information regarding the overall setup. For each i-th simulation the following files are provided:</p> <ul> <li>x_{i}.npy - array with sheet trajectories (#time-steps, #sheets)</li> <li>v_{i}.npy - array with sheet velocities (#time-steps, #sheets)</li> <li>x_eq_{i}.npy - array with sheet equilibrium positions (#time-steps, #sheets)</li> </ul> <p> Model sub-folders are named according to :</p> <ul> <li>models/{time step}/{seed} - default architecture (preferred)</li> <li>models/{time step}/{'collisions', 'nosent' or 'equivariant'}/{seed} - alternative (less performing) architectures mentioned in the paper appendices.</li> </ul> <p>For each model we provide:</p> <ul> <li>params_best.pkl - model weights that performed the best during training on the validation set</li> <li>params_final.pkl - model weights at the end of training</li> <li>model_cfg.yml - GNN architecture metadata</li> <li>train_cfg.yml - training configuration metadata</li> <li>train_data.yml - training dataset metadata</li> <li>loss.txt - training and validation loss per epoch</li> <li>loss_i.txt - training loss per gradient update step</li> </ul> <h3>Source Code</h3> <p>The source code used to produce the data, train, and test the models can be found at: <a href="https://github.com/diogodcarvalho/gns-sheet-model">https://github.com/diogodcarvalho/gns-sheet-model</a></p> <h3>References</h3> <p>[1] D. D. Carvalho, D. R. Ferreira, L. O. Silva, "Learning the dynamics of a one-dimensional plasma model with graph neural networks<em>", Mach. Learn.: Sci. Technol. 5 025048 </em>(2024)</p> <p>[2] J. Dawson, "One‐Dimensional Plasma Model"<em>, The Physics of Fluids</em> 5.4 (1962): 445-459.</p> <p> </p>
Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"
<p>Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.</p> <p>PAPER</p> <p>https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032</p> <p>CODE and MODELS:</p> <p>The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at <a href="https://github.com/jule-c/ET-Tox">https://github.com/jule-c/ET-Tox</a>, and the processed data as well as pretrained models for training and testing can be downloaded from <a href="../record/7942946">https://zenodo.org/record/7942946</a>. We can provide the full list of conformers as XYZ files upon request.</p>
CompanyKG Dataset V2.0: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
<p><strong>CompanyKG</strong> is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.</p> <p><strong>Edges</strong>: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.</p> <p><strong>Nodes</strong>: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "<em>Klarna is a fintech company that provides support for direct and post-purchase payments</em> ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: <a href="https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2">mSBERT</a> (multilingual Sentence BERT), <a href="https://platform.openai.com/docs/guides/embeddings/what-are-embeddings">ADA2</a>, <a href="https://github.com/princeton-nlp/SimCSE">SimCSE</a> (fine-tuned on the raw company descriptions) and <a href="https://github.com/EQTPartners/pause">PAUSE</a>.</p> <p><strong>Evaluation Tasks</strong>. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:</p> <ul> <li><strong>Similarity Prediction (SP)</strong>. To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.</li> <li><strong>Competitor Retrieval (CR)</strong>. Each sample contains one <em>target company</em> and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with <em>N</em> direct competitors in this CR evaluation set, we expect a competent method to retrieve all <em>N</em> competitors when searching for similar companies to A. </li> <li><strong>Similarity Ranking (SR)</strong> is designed to assess the ability of any method to rank <em>candidate companies</em> (numbered 0 and 1) based on their similarity to a <em>query company</em>. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development. </li> <li><strong>Edge Prediction (EP)</strong> evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).</li> </ul> <p><strong>Background and Motivation</strong></p> <p>In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as <strong>company similarity quantification</strong>, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.</p> <p>While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment. </p> <p>In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.</p> <p>However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a <strong>Knowledge Graph (KG)</strong>. Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.</p> <p><strong>Source Code and Tutorial:<br></strong><a href="https://github.com/llcresearch/CompanyKG2"><strong>https://github.com/llcresearch/CompanyKG2</strong></a></p> <p><strong>Paper: to be published<br></strong></p>
ORBITAAL: cOmpRehensive BItcoin daTaset for temporAl grAph anaLysis
<h3>Dataset Construction</h3> <p>This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (<a href="https://github.com/blockchain-etl/bitcoin-etl">https://github.com/blockchain-etl/bitcoin-etl</a>) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by <a href="https://www.walletexplorer.com/">https://www.walletexplorer.com/</a></p> <p>[1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," <em>2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)</em>, Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.<br>keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain},</p> <p> </p> <h3>Dataset Description</h3> <p><strong>Bitcoin Activity Temporal Coverage</strong>: From 03 January 2009 to 25 January 2021</p> <h4>Overview:</h4> <p>This <strong>dataset </strong>provides a <strong>comprehensive</strong> representation of <strong>Bitcoin exchanges</strong> between entities over a s<strong>ignificant temporal span</strong>, spanning from the inception of Bitcoin to recent years. It encompasses <strong>various temporal resolutions</strong> and <strong>representations</strong> to <strong>facilitate Bitcoin transaction network analysis </strong>in the context of <strong>temporal graphs</strong>.</p> <p>Every dates have been retrieved from bloc UNIX timestamp and GMT timezone.</p> <h4>Contents:</h4> <p>The dataset is distributed across three compressed archives:</p> <p>All data are stored in the <strong>Apache Parquet file format</strong>, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package.</p> <ol> <li> <p><strong>orbitaal-stream_graph.tar.gz</strong>:</p> <ul> <li>The root directory is <em>STREAM_GRAPH/</em></li> <li>Contains a <strong>stream graph</strong> representation of Bitcoin exchanges at the <strong>finest temporal scale</strong>, corresponding to the validation time of <strong>each block</strong> (averaging approximately 10 minutes).</li> <li>The stream graph is divided into 13 files, one for each year</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet,</strong> where <em>[YYYY]</em> stands for the corresponding <em>year</em> and <em>[ID]</em> is <em>an integer</em> from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>STREAM_GRAPH/EDGES/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-all.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>snapshot</strong> network representing <strong>all transactions aggregated </strong>over the whole dataset period (from Jan. 2009 to Jan. 2021).</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-all.snappy.parquet</strong>.</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/ALL/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-year.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>yearly</strong><em> </em>resolution of <strong>snapshot</strong> networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet</strong>, where <em>[YYYY]</em> stands for the corresponding <em>year </em>and <em>[ID]</em> is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/year/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-month.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>monthly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY] </em>and <em>[MM] </em>stands for the corresponding <em>year </em>and <em>month, </em>and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/month/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-day.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>daily </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, and <em>[DD] </em>stand for the corresponding <em>year</em>, <em>month</em>, and <em>day</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/day/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-hour.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>hourly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, <em>[DD]</em>, and <em>[hh]</em> stand for the corresponding <em>year, month, day, </em>and <em>hour</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/hour/</em></li> </ul> </li> <li> <p><strong>orbitaal-nodetable.tar.gz</strong>:</p> <ul> <li>The root directory is <em>NODE_TABLE/</em></li> <li>Contains two files in parquet format, the first one gives <strong>information </strong>related to <strong>nodes </strong>present in stream graphs and snapshots such as <strong>period of activity</strong> and associated global <strong>Bitcoin balance</strong>, and the other one contains the list of <strong>all associated Bitcoin addresses.</strong></li> </ul> </li> </ol> <p> </p> <p>Small samples in CSV format</p> <ol> <li> <p><strong>orbitaal-stream_graph-2016_07_08.csv</strong> and <strong>orbitaal-stream_graph-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to stream graph representations of an halvening happening in 2016.</li> </ul> </li> <li> <p><strong>orbitaal-snapshot-2016_07_08.csv </strong>and<strong> orbitaal-snapshot-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to daily snapshot representations of an halvening happening in 2016.</li> <li><strong> </strong></li> </ul> </li> </ol> <p> </p> <p> </p> <p> </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)
<p>With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied. </p>
A biodiversity dataset graph: Biodiversity Heritage Library (BHL)
<p>A biodiversity dataset graph: Biodiversity Heritage Library </p> <p>Biodiversity datasets, or descriptions of biodiversity datasets, are increasingly available through open digital data infrastructures such as the Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org). "The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community." - https://biodiversitylibrary.org , June 2019. </p> <p>However, little is known about how these networks, and the data accessed through them, change over time. This dataset provide snapshots of all OCR item texts (e.g., individual items) available through BHL as tracked by Preston (https://github.com/bio-guoda/preston , https://doi.org/10.5281/zenodo.1410543 ) over period May - June 2019.</p> <p>This snapshot contains about 120GB of uncompressed OCR texts across 227k OCR BHL items. Also, a snapshot of the BHL item catalog at https://www.biodiversitylibrary.org/data/item.txt is included.</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to eestablish a versioning mechanism. Provenance files describe how, when and where the BHL OCR text items were retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543). </p> <p>To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. After that, verify the index of the archive by reproducing the following result:</p> <p>$ java -jar preston.jar history<br> <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca OK CONTENT_PRESENT_VALID_HASH 49458087<br> hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 OK CONTENT_PRESENT_VALID_HASH 25745<br> hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c OK CONTENT_PRESENT_VALID_HASH 519892</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". </p> <p>Files in this data publication:</p> <p>README - this file</p> <p>preston-[00-ff].tar.gz - preston archives containing BHL OCR item texts, their provenance and a provenance index.</p> <p>9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70 - preston index file<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a - preston index file</p> <p>89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a - preston provenance file<br> 41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4 - preston provenance file<br> </p> <p>This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.</p>
A biodiversity dataset graph: BHL
<p>A biodiversity dataset graph: BHL</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the Biodiversity Heritage Library (BHL). The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community. </p> <p>This dataset provides versioned snapshots of the BHL network as tracked by Preston [2] between 2019-05-13 and 2019-10-07 using "preston update -u https://biodiversitylibrary.org". </p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the BHL content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://zenodo.org/record/3484555/files</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> <http://purl.org/pav/previousVersion> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> .<br> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> <http://purl.org/pav/previousVersion> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> .<br> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> <http://purl.org/pav/previousVersion> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> .<br> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> <http://purl.org/pav/previousVersion> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> .<br> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca OK CONTENT_PRESENT_VALID_HASH 49458087<br> hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 OK CONTENT_PRESENT_VALID_HASH 25745<br> hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c OK CONTENT_PRESENT_VALID_HASH 519892<br> hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 OK CONTENT_PRESENT_VALID_HASH 787414</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". </p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README </p> <p>-- executable java jar containing preston[2] v0.1.8. --<br> preston.jar</p> <p>-- preston archives containing BHL data files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz </p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br> 2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800<br> 4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f<br> 6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c<br> 82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3<br> 9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70<br> bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2</p> <p>--- end of file descriptions ---</p> <p><br> References </p> <p>[1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-13 to 2019-10-07 with provenance hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . </p> <p><br> This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.</p>
Datasets for "MGTCOM: Community Detection in Multimodal Graphs"
<p>These are the datasets used in <em><strong>"MGTCOM: Community Detection in Multimodal Graphs"</strong></em></p> <p>The dataset preparation code can be found in <a href="https://github.com/EgorDm/MGTCOM">our repository</a>.</p> <p>Each dataset consists of a heterogenous graph with additional edge or node timestamps and preprocessed feature vectors.</p> <p>For each dataset, the files are split into raw and processed folders.<br> * `<em>raw</em>` folder: contains a set of parquet files with formatted raw dataset data. Files follow the naming convention `node_<name>` or `edge_<from>_<rel_name>_<to>`.<br> * `<em>processed</em>` folder: contains preprocessed datasets in <a href="https://pytorch-geometric.readthedocs.io/en/latest/">pytorch geometric</a> graph data format</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
MTL-QA : A dataset and multi-task learning approach for knowledge graph and natural language question answering
<p>The dataset used for this project is created by enhancing the publicly available MetaQA (Movie Text Audio QA), which is primarily a KGQA dataset pertaining to movies, an extension of WikiMovies. This involves questions requiring 1, 2, and 3 hops which can be answered by using a MetaQA Knowledge Graph. The questions are available in text and audio format. The text has vanilla (original) and its paraphrased version, and is called ntm. </p> <p>In order to develop a dataset to support NLQA, a series of dataset augmentation steps has been performed.</p> <p>The dataset consists of natural language questions and a tagged topic entity as ground truth. This topic entity is used to retrieve textual information related to the question from Wikipedia. The introduction section of the entity's page is used as the context that is required for NLQA. Hence, this dataset has information related to both KGQA and NLQA. Certain preliminary checks and validations are done to only retain those data samples whose context can be used to answer a given question.</p>
Dataset of globally rigid graphs
<p>This data set collectes all globally rigid graphs for dimension d<=25 with at most d+k vertices, where k might be different.<br> The graphs are collected in a zip file with respect to the dimension. Graphs are stored in graph6 data format.</p>
A biodiversity dataset graph: GBIF, iDigBio, BioCASe hash://sha256/450deb8ed9092ac9b2f0f31d3dcf4e2b9be003c460df63dd6463d252bff37b55 hash://md5/898a9c02bedccaea5434ee4c6d64b7a2
<p>A biodiversity dataset graph: GBIF, iDigBio, BioCASe hash://sha256/450deb8ed9092ac9b2f0f31d3dcf4e2b9be003c460df63dd6463d252bff37b55 hash://md5/898a9c02bedccaea5434ee4c6d64b7a2</p> <p>The intended use of this archive is to facilitate meta-analysis of the Global Biodiversity Information Facility, Integrated Digitized Biocollections, Biological Collection Access Service (GBIF, iDigBio, BioCASe). GBIF, iDigBio and BioCASe help provide access to biological data collections.</p> <p>This dataset provides versioned provenance logs of snapshots of the GBIF, iDigBio, BioCASe network as tracked by Preston [2] between 2018-09-03 and 2023-02-02 using "preston update -u https://gbif.org,https://idigbio.org,http://biocase.org".</p> <p>This publication contains two types of files: index files and provenance logs. Associated data files are hosted elsewhere for pragmatic reasons. Index files provide a way to link provenance files in time to establish a versioning mechanism. Provenance logs describe how, when, what and where the GBIF, iDigBio, BioCASe content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded GBIF, iDigBio, BioCASe biodiversity dataset graph, use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar ls --remote https://zenodo.org/record/7651831/files > /dev/null</p> <p>Optionally, you can retrieve all associated data (>500GB) files using:</p> <p>$ java -jar preston.jar clone https://zenodo.org/record/7651831/files --remote https://zenodo.org/record/7651831/files,https://linker.bio,https://archive.org/download/biodiversity-dataset-archives/data.zip/data/</p> <p>Please note https://archive.org/download/biodiversity-dataset-archives/data.zip/data/ and https://linker.bio are Preston remotes that provided access to GBIF, iDigBio, BioCASe data files at time of writing (17 Feb 2023). These remotes can replaced with any other Preston remote(s) if needed. This may take a while depending on network speed and hardware constraints. See also https://archive.org/details/biodiversity-dataset-archives .</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history</p> <p><hash://sha256/450deb8ed9092ac9b2f0f31d3dcf4e2b9be003c460df63dd6463d252bff37b55> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1621a777e4a7442e9864424820c5f825d9cf1c65599cbfbbda039384f1b74ada> .<br> <hash://sha256/1621a777e4a7442e9864424820c5f825d9cf1c65599cbfbbda039384f1b74ada> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/b1c11d9231768def925b9d076c1c4b711a727326ad99e62982aa4ede288e5aa2> .<br> <hash://sha256/b1c11d9231768def925b9d076c1c4b711a727326ad99e62982aa4ede288e5aa2> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/125e67d7c5077af8fa958569644d61e44a39bbbbdaaf16af0430dcf441e05cec> .<br> <hash://sha256/125e67d7c5077af8fa958569644d61e44a39bbbbdaaf16af0430dcf441e05cec> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/63fae82e8a3aacd11e4a06b5736242aabe40802c6259a38de066de14848e3718> .<br> <hash://sha256/63fae82e8a3aacd11e4a06b5736242aabe40802c6259a38de066de14848e3718> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/7cd305e9d275763c96e7685847460fcc381b5c97c1460c00441f663c1788800f> .<br> <hash://sha256/7cd305e9d275763c96e7685847460fcc381b5c97c1460c00441f663c1788800f> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/38e8e17f6742d39379b96cec2d4e70a5a63a85a28aee49727031c9061f4b1e03> .<br> <hash://sha256/38e8e17f6742d39379b96cec2d4e70a5a63a85a28aee49727031c9061f4b1e03> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86> .<br> <hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/aab08c5c87ce6a8f400972e2b09b7fa3421947b59407a8feb98388d7e42b49e8> .<br> <hash://sha256/aab08c5c87ce6a8f400972e2b09b7fa3421947b59407a8feb98388d7e42b49e8> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/f449a34dd80d4e33248a1a7cb0d0fa2b8dac49865a0a32ed5bbaacb22addb0d1> .<br> <hash://sha256/f449a34dd80d4e33248a1a7cb0d0fa2b8dac49865a0a32ed5bbaacb22addb0d1> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/b771ed09aea78055e39d5c955997e5d9b42dd9edc6b094d9b8a27df16bdc6b6c> .<br> <hash://sha256/b771ed09aea78055e39d5c955997e5d9b42dd9edc6b094d9b8a27df16bdc6b6c> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/15edbac974fb77347e07cda76358f7f662dd800bfc5b3e476fc66ecdc6203d03> .<br> <hash://sha256/15edbac974fb77347e07cda76358f7f662dd800bfc5b3e476fc66ecdc6203d03> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/4000d2a1af6da5b46f374038d884f91768782a1905d4a75fff3c8c3bb6629913> .<br> <hash://sha256/4000d2a1af6da5b46f374038d884f91768782a1905d4a75fff3c8c3bb6629913> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/f6d133620a665569a13a3fb7ca31b163bf849864812d447238994226d35e3253> .<br> <hash://sha256/f6d133620a665569a13a3fb7ca31b163bf849864812d447238994226d35e3253> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/e10c234ac54f02fd63da87b418f36428b876d91a30a42a4657e1726ba862b900> .<br> <hash://sha256/e10c234ac54f02fd63da87b418f36428b876d91a30a42a4657e1726ba862b900> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/83b4553edfc58e6389d427a08de533236e6a7eeb39b61239d225b0d4188d8c84> .<br> <hash://sha256/83b4553edfc58e6389d427a08de533236e6a7eeb39b61239d225b0d4188d8c84> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/49e079a0bac47ca17c0b14fa711b7742b9332ac64e1866adf13d294692720f9f> .<br> <hash://sha256/49e079a0bac47ca17c0b14fa711b7742b9332ac64e1866adf13d294692720f9f> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/000cd23a8494a8a18f8b552e7f113af418eb2ae85e9908f61f44c720ce70608b> .<br> <hash://sha256/000cd23a8494a8a18f8b552e7f113af418eb2ae85e9908f61f44c720ce70608b> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1cce63489aa8618dcaf19ce2cd6166a7ba801798b235a25a725397d38c2fe957> .<br> <hash://sha256/1cce63489aa8618dcaf19ce2cd6166a7ba801798b235a25a725397d38c2fe957> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417> .<br> <hash://sha256/251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/810b22c16e1a3911c6eecfca348758d3ffd5b29fc36990015cda6427bdde2233> .<br> <hash://sha256/810b22c16e1a3911c6eecfca348758d3ffd5b29fc36990015cda6427bdde2233> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/dd16f4bae9a02ce71bc3ba4da2809cc5035743a4e23f61f5631f69b08d0e40f5> .<br> <hash://sha256/dd16f4bae9a02ce71bc3ba4da2809cc5035743a4e23f61f5631f69b08d0e40f5> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/bf676e2ce4164f8148a793188650f07c464dc52b2bfc07e92c9f16041baba8d5> .<br> <hash://sha256/bf676e2ce4164f8148a793188650f07c464dc52b2bfc07e92c9f16041baba8d5> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1fd3e156c6ba1632a27b2bebaea36f76afeac8dfecf530d772988832821304ea> .<br> <hash://sha256/1fd3e156c6ba1632a27b2bebaea36f76afeac8dfecf530d772988832821304ea> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/4dbee404b74775cac279e0e7fbc1aa72dddfc70df02b07b9a2f82023dccd4732> .<br> <hash://sha256/4dbee404b74775cac279e0e7fbc1aa72dddfc70df02b07b9a2f82023dccd4732> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1f25e9a78ad0630ead9676807269185761f0d23544a4492a0337c2d306b10686> .<br> <hash://sha256/1f25e9a78ad0630ead9676807269185761f0d23544a4492a0337c2d306b10686> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/20b07c79ff0d48c818e2882816948ed192d5c86bdff2118881d7446b15e63bf1> .<br> <hash://sha256/20b07c79ff0d48c818e2882816948ed192d5c86bdff2118881d7446b15e63bf1> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/6b7bc4dc5901a459663f47628768b53622eda36bd0fa092390c6d1c0323abf6d> .<br> <hash://sha256/6b7bc4dc5901a459663f47628768b53622eda36bd0fa092390c6d1c0323abf6d> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/d98e3bd2bc717bc11a3338cd43fc488bde1d96cb42d8cbe8301f0d9f9753007f> .<br> <hash://sha256/d98e3bd2bc717bc11a3338cd43fc488bde1d96cb42d8cbe8301f0d9f9753007f> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1d184ff657913a77d50b9f33b5bd1f483220fd83f26dbf02c020f98c778aafae> .<br> <hash://sha256/1d184ff657913a77d50b9f33b5bd1f483220fd83f26dbf02c020f98c778aafae> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/a35ec845ec71a2951652d70e574e6280c843879efc3b1639e9ccdb4fbfd45e69> .<br> <hash://sha256/a35ec845ec71a2951652d70e574e6280c843879efc3b1639e9ccdb4fbfd45e69> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead> .<br> <hash://sha256/8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/afc2a4caa7f07503ccda9154d34dea1852c8283dee5cb4c5df7ddb3ce238ab7d> .<br> <hash://sha256/afc2a4caa7f07503ccda9154d34dea1852c8283dee5cb4c5df7ddb3ce238ab7d> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/5cf1a9e491f218a94af5439f90beb905ae923f94cdd85c542d85c74c241f9e6e> .<br> <hash://sha256/5cf1a9e491f218a94af5439f90beb905ae923f94cdd85c542d85c74c241f9e6e> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/8aacce08462b87a345d271081783bdd999663ef90099212c8831db399fc0831b> .<br> <hash://sha256/8aacce08462b87a345d271081783bdd999663ef90099212c8831db399fc0831b> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/f13b15a20e4fe70b4a111e67ac20ef676404b8456dfc39694f2cb3a4c62a2b2d> .<br> <hash://sha256/f13b15a20e4fe70b4a111e67ac20ef676404b8456dfc39694f2cb3a4c62a2b2d> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/3b39831bcc286c1db44787e21b736378f5847a16b7c39bdac3dd2011e9189dc1> .<br> <hash://sha256/3b39831bcc286c1db44787e21b736378f5847a16b7c39bdac3dd2011e9189dc1> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/916255b2b73680595dcb22b30991a757dd223208473fb4fbe90405757bc07953> .<br> <hash://sha256/916255b2b73680595dcb22b30991a757dd223208473fb4fbe90405757bc07953> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/39f83f5805f32f765003c5e9ee8c69adb3889d9f26dd61bf4aa3a829ac744e2c> .<br> <hash://sha256/39f83f5805f32f765003c5e9ee8c69adb3889d9f26dd61bf4aa3a829ac744e2c> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/5dcf876c6cb0c5b15197acf1ea6989d41c1a1333c6a7e0437f035aa9d22a3790> .<br> <hash://sha256/5dcf876c6cb0c5b15197acf1ea6989d41c1a1333c6a7e0437f035aa9d22a3790> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/9c17ce013b33c3c9e6bc513cb49a14660fad9bd6f87a4f21568cc871b10ba39b> .<br> <hash://sha256/9c17ce013b33c3c9e6bc513cb49a14660fad9bd6f87a4f21568cc871b10ba39b> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/6c4c94cdb224d39e7c655b1a1a6afbba8daf3c9ac64c42ba72dfd346d5d3a547> .<br> <hash://sha256/6c4c94cdb224d39e7c655b1a1a6afbba8daf3c9ac64c42ba72dfd346d5d3a547> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/ff74959ec6e5e98e7db674afcb915f50725f049b968e9a9f10de169aa0a3dcb5> .<br> <hash://sha256/ff74959ec6e5e98e7db674afcb915f50725f049b968e9a9f10de169aa0a3dcb5> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/ab62f4a9601f30d23353a479830f9d2dfc7898e15d2cc2d81977e898d885c908> .<br> <hash://sha256/ab62f4a9601f30d23353a479830f9d2dfc7898e15d2cc2d81977e898d885c908> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/6fb7271a2da1543036e39bcdb4c415a46b5437569eaaf0ffdef3e907a2f4309f> .<br> <hash://sha256/6fb7271a2da1543036e39bcdb4c415a46b5437569eaaf0ffdef3e907a2f4309f> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/d79fb9207329a2813b60713cf0968fda10721d576dcb7a36038faf18027eebc1> .<br> <hash://sha256/d79fb9207329a2813b60713cf0968fda10721d576dcb7a36038faf18027eebc1> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/6387c9ebed9507a0fbba2d161e83c2da73e0d6fa6dd51fb19ac4a4ca75b839c7> .<br> <hash://sha256/6387c9ebed9507a0fbba2d161e83c2da73e0d6fa6dd51fb19ac4a4ca75b839c7> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/668d5d6e9c9e7ddb410073ff75eb7f2935c60cc62944ba1fd96ca60feec4a103> .<br> <hash://sha256/668d5d6e9c9e7ddb410073ff75eb7f2935c60cc62944ba1fd96ca60feec4a103> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/09574d9c1330c2b1bec9b7bf3a55ab9273bedbfed78affd70a058a1a25e052d2> .<br> <hash://sha256/09574d9c1330c2b1bec9b7bf3a55ab9273bedbfed78affd70a058a1a25e052d2> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd> .<br> <hash://sha256/b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e> .<br> <hash://sha256/9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/af8f9ed321d9c403617f54a96e3217adc918970fbbfe8b8715359669f4890b63> .<br> <hash://sha256/af8f9ed321d9c403617f54a96e3217adc918970fbbfe8b8715359669f4890b63> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/5a39b7bbe9d1bc46ed2eb7bd76c490b5c85a09369a7cf7dc18fa04532679e9a7> .<br> <hash://sha256/5a39b7bbe9d1bc46ed2eb7bd76c490b5c85a09369a7cf7dc18fa04532679e9a7> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12> .<br> <hash://sha256/261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/af0bb2c89571a30815d4488e72dede84a2ffc102bb87961f06884509fd5d1dae> .<br> <hash://sha256/af0bb2c89571a30815d4488e72dede84a2ffc102bb87961f06884509fd5d1dae> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/d99fa37caa268f8061980001146ed2a566e814d0740bb1974b76847512be95d3> .<br> <hash://sha256/d99fa37caa268f8061980001146ed2a566e814d0740bb1974b76847512be95d3> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/8c0752dc6425b9c716837c9713ce284158b4cff70a1e66be2beb0677018831f4> .<br> <hash://sha256/8c0752dc6425b9c716837c9713ce284158b4cff70a1e66be2beb0677018831f4> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/0e6b49850d96b4b58ea3759ecea45d273a48f074c4edaaec5e008791d7718781> .<br> <hash://sha256/0e6b49850d96b4b58ea3759ecea45d273a48f074c4edaaec5e008791d7718781> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/a7a5e7c6a4b21bdf67f48d6bea85f438b8133f674027b04625dfadec3ff985f6> .<br> <hash://sha256/a7a5e7c6a4b21bdf67f48d6bea85f438b8133f674027b04625dfadec3ff985f6> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/a0a18b0e32f933112084b846863438038f66f63eeeb22fa9d8d734e8a25bb208> .<br> <hash://sha256/a0a18b0e32f933112084b846863438038f66f63eeeb22fa9d8d734e8a25bb208> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/58daa9a51e5dc0911163aa1b98d68c801106734cd29eab9980814057351aeb70> .<br> <hash://sha256/58daa9a51e5dc0911163aa1b98d68c801106734cd29eab9980814057351aeb70> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/480868b59e95f3ce2324a7308dba65795e857d34cfbdcea7440a6f2620c6fbf6> .<br> <hash://sha256/480868b59e95f3ce2324a7308dba65795e857d34cfbdcea7440a6f2620c6fbf6> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/3dd674b7ad16391629948981a9cb6f6f86937d016861c3e59cd6e6bf3589f3b7> .<br> <hash://sha256/3dd674b7ad16391629948981a9cb6f6f86937d016861c3e59cd6e6bf3589f3b7> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/eb2c716ec85158a0785216de1b09965173fc368d12f213c1bf747bbc2e49c6a6> .<br> <hash://sha256/eb2c716ec85158a0785216de1b09965173fc368d12f213c1bf747bbc2e49c6a6> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/5c469224fa0b6159bf33a59ddaa0246634e81bddd1728e7bf3540745055eccfa> .<br> <hash://sha256/5c469224fa0b6159bf33a59ddaa0246634e81bddd1728e7bf3540745055eccfa> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/3e7f19a8a78b51437240f49c499e6e7f89b8d58d4e3ceb9480d4356721645cee> .<br> <hash://sha256/3e7f19a8a78b51437240f49c499e6e7f89b8d58d4e3ceb9480d4356721645cee> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/e69d186ff3be11830c2da67d1bfeb896ec6398fc9d555fa26eaae1baa54450fb> .<br> <hash://sha256/e69d186ff3be11830c2da67d1bfeb896ec6398fc9d555fa26eaae1baa54450fb> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/e4e5736e8bfec6c686eedde4c6dfa62845930d04e12dfa6f8a7d70abc3d087df> .<br> <hash://sha256/e4e5736e8bfec6c686eedde4c6dfa62845930d04e12dfa6f8a7d70abc3d087df> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/650a28fff3e03dadba70dc05a34c580c04203380187953fa4a2fb778353fee79> .<br> <hash://sha256/650a28fff3e03dadba70dc05a34c580c04203380187953fa4a2fb778353fee79> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/f3ed6aa1bd15ee43d05e138b935040aaa745f6ca8c7e8f2dfbb0a3ae0df66f36> .<br> <hash://sha256/f3ed6aa1bd15ee43d05e138b935040aaa745f6ca8c7e8f2dfbb0a3ae0df66f36> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/fd27b0552c8a6800a8b3b1b822a2063a3215c1d9887badad09a62746b80846bc> .<br> <hash://sha256/fd27b0552c8a6800a8b3b1b822a2063a3215c1d9887badad09a62746b80846bc> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/102cbfb1e800ef795ba1e1c51a34bff9b463b34c9443435069ddc76970c1e9c9> .<br> <hash://sha256/102cbfb1e800ef795ba1e1c51a34bff9b463b34c9443435069ddc76970c1e9c9> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/ba02b235fd445904eae45b50bc637a195f25e9ca1637bcf26b2dc7f8698aa1fe> .<br> <hash://sha256/ba02b235fd445904eae45b50bc637a195f25e9ca1637bcf26b2dc7f8698aa1fe> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/1d3817d9cb9fc7de7a3b7a4181daba8de1e52b348280154e8a163c7dd7ee1a7e> .<br> <hash://sha256/1d3817d9cb9fc7de7a3b7a4181daba8de1e52b348280154e8a163c7dd7ee1a7e> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/b5a30bbd8d51e9faf08d4ddebbc5bda9bab1b12545172f1524ac5ebdb0038bd4> .<br> <hash://sha256/b5a30bbd8d51e9faf08d4ddebbc5bda9bab1b12545172f1524ac5ebdb0038bd4> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/05a877bdb8617144fe166a13bf51828d4ad1bc11631c360b9e648a9f7df2bbcd> .<br> <hash://sha256/05a877bdb8617144fe166a13bf51828d4ad1bc11631c360b9e648a9f7df2bbcd> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/7efdea9263e57605d2d2d8b79ccd26a55743123d0c974140c72c8c1cfc679b93> .<br> <hash://sha256/7efdea9263e57605d2d2d8b79ccd26a55743123d0c974140c72c8c1cfc679b93> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/b83cf099449dae3f633af618b19d05013953e7a1d7d97bc5ac01afd7bd9abe5d> .<br> <hash://sha256/b83cf099449dae3f633af618b19d05013953e7a1d7d97bc5ac01afd7bd9abe5d> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/c253a5311a20c2fc082bf9bac87a1ec5eb6e4e51ff936e7be20c29c8e77dee55> .<br> <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/c253a5311a20c2fc082bf9bac87a1ec5eb6e4e51ff936e7be20c29c8e77dee55> .</p> <p><br> If you retrieved data files, you can check the integrity of the extracted archive by confirming that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/3eff98d4b66368fd8d1f8fa1af6a057774d8a407a4771490beeb9e7add76f362 file:/home/preston/preston-archive/data/3e/ff/3eff98d4b66368fd8d1f8fa1af6a057774d8a407a4771490beeb9e7add76f362 OK CONTENT_PRESENT_VALID_HASH 89931<br> hash://sha256/184886cc6ae4490a49a70b6fd9a3e1dfafce433fc8e3d022c89e0b75ea3cda0b file:/home/preston/preston-archive/data/18/48/184886cc6ae4490a49a70b6fd9a3e1dfafce433fc8e3d022c89e0b75ea3cda0b OK CONTENT_PRESENT_VALID_HASH 210344<br> hash://sha256/1846abf2b9623697cf9b2212e019bc1f6dc4a20da51b3b5629bfb964dc808c02 file:/home/preston/preston-archive/data/18/46/1846abf2b9623697cf9b2212e019bc1f6dc4a20da51b3b5629bfb964dc808c02 OK CONTENT_PRESENT_VALID_HASH 210344<br> hash://sha256/554fdab07f2372bf363a1d7ef30fcf4c32e1da98b95a6342780c5eb35e0e7b38 file:/home/preston/preston-archive/data/55/4f/554fdab07f2372bf363a1d7ef30fcf4c32e1da98b95a6342780c5eb35e0e7b38 OK CONTENT_PRESENT_VALID_HASH 202701</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston[2] v0.5.4 --<br> preston.jar</p> <p><br> --- end of file descriptions ---</p> <p>References</p> <p>[1] Global Biodiversity Information Facility, Integrated Digitized Biocollections, Biological Collection Access Service (GBIF, iDigBio, BioCASe, https://gbif.org,https://idigbio.org,http://biocase.org) accessed from 2018-09-03 to 2023-02-02 with provenance hash://sha256/450deb8ed9092ac9b2f0f31d3dcf4e2b9be003c460df63dd6463d252bff37b55 hash://md5/898a9c02bedccaea5434ee4c6d64b7a2 .<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .</p> <p>This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation</p>
Dataset Graph 6: Stagings of Heiner Müller's Plays in Germany (East and West).
<p>Dataset for Graph 6. Data collected based on Ludwig, Janine. Heiner Müller, Ikone West: Das dramatische Werk Heiner Müllers in der Bundesrepublik; Rezeption und Wirkung. Frankfurt am Main: Lang, 2009, 53–4.</p> <p>Graph 6 shows the number of stagings of Heiner Müller’s plays in East and West Germany from 1957-1990.</p>
Dataset Graphs 3-5: Publication Types for Heiner Müller's Writings 1967–1977, 1978-1988, 1989-98.
<p>Dataset for Graphs 3-5. Data collected based on the Heiner Müller Werkausgabe.</p> <p>Graphs 3-5 shows the publication types that Heiner Müller used to publish his writing in the years from 1967–1977, 1978-1988, 1989-98.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.