This dataset contains in-air hand-written numbers and shapes data used in the paper:B. Alwaely and C. Abhayaratne, "Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition," in IEEE Access, vol. 7, pp. 159661-159673, 2019, doi: 10.1109/ACCESS.2019.2950643.The dataset contains the following:-Readme.txt- InAirNumberShapeDataset.zip containing-Number Folder (With 2 sub folders for Matlab and Excel)-Shapes Folder (With 2 sub folders for Matlab and Excel)The datasets include the in-air drawn number and shape hand movement path captured by a Kinect sensor. The number sub dataset includes 500 instances per each number 0 to 9, resulting in a total of 5000 number data instances. Similarly, the shape sub dataset also includes 500 instances per each shape for 10 different arbitrary 2D shapes, resulting in a total of 5000 shape instances. The dataset provides X, Y, Z coordinates of the hand movement path data in Matlab (M-file) and Excel formats and their corresponding labels.This dataset creation has received The University of Sheffield ethics approval under application #023005 granted on 19/10/2018.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry. Finally, we also have done some in a Structural Bioinformatics, Bio-Image analysis and retrieval.

Exports of Finished Metal Shapes in the United States decreased to 2226.80 USD Million in February from 2272.62 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Finished Metal Shapes.

We have curated a dataset of 3D scans of objects from existing data repositories. All scans are of quadruped mammals. Shapes were primarily created using a variety of capturing techniques. Ground-truth corresdpondences were then established by three experts. Shapes (e.g., a giraffe and a dog) exhibit substantial non-isometric deformation, which prove challenging for existing shape matching methods. The dataset consists of both partial and watertight (i.e., full) meshes. Additional complexities caused by scanning, such as geometric and topological change, are also present. Pairs of shapes are assigned to test-sets of gradually increasing non-isometry.This dataset has been split into two parts to help make the licences clearer. This part contains the following shapes: bison; dog; elephant_a; giraffe_a; hippo; leopard; pig; rhino.Licence applicable to relevant part of data:Name in datasetOriginal nameAuthorLicenseHyperlinkBisonBisonMisterdeviousCC BY-NC-SA 4.0linkDogPlastic Toy Dog -RAWscanSpognaCC BY-NC 4.0linkElephant_aPlastic ElephantSpognaCC BY-NC 4.0linkGiraffe_aGiraffe Scanned by EinScan-S 3D ScannerShinning 3DCC BY 4.0linkhippoBronze hippoAlbanCC BY 4.0linkleopardCheetahMoshe CaineCC BY 4.0linkpigPigLindyCC BY 4.0linkrhinoModel 56B - Southern White Rhino - Mesh OnlyDigitalLife3DCC BY 4.0link

In the frame of the EU-funded project "FleX-RAY", an inherently flexible x-ray, imaging detector using single photon avalanche photodiodes and inorganic scintillating fibres was developed. For the monitoring of the detector's shape, an optical 3D shape sensor is mounted on the x-ray detector. The 3D data from this data set can be used in combination with the x-ray detection data to generate an x-ray image corrected for the bending of the x-ray detector. This archive contains the following data:(1) Sensor Description: The material, manufacturing, and layout of the sensor are described here. This information is relevant to understand the following data.(2) Sensor Data: The linear calibration function for each Bragg grating from a two-point calibration is stated. The Bragg wavelengths at a curvature of -17.3/m (bent around the y-axis) and the respective curvatures (recalculated from the calibration function) are given. This data can be used to generate the 3D shape data.(3) 3D Graph: The curvatures are interpolated and extrapolated to give a 3D graph. The 3D graph shows the 3D shape of the detector. More information on the measurement system, the data acquisition, the calibration process, and the shape calculation can be found in the following publication:J. Koch, A. Droste, M. Angelmahr, G. Flachenecker, W. Schade, 3D shape-sensor based on integrated optics in ultra-thin glass, Proc. SPIE 12424, Integrated Optics: Devices, Materials, and Technologies XXVII, 1242417 (17 March 2023). DOI: 10.1117/12.2650357 This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 899634.

Regular path queries (RPQs) are at the heart of navigational queries in graph databases. Motivated by new features of regular path queries in the languages Cypher, GQL, and SQL/PGQ, which require new approaches for indexing and compactly storing intermediate query results, we investigate a large corpus of real-world RPQs. Our corpus consists of 148.7 million RPQs occurring in 937.2 million SPARQL queries, used on 29 different data sets.

We investigate three main questions on these logs. First, what is the syntactic structure of RPQs in practice? Second, how much non-determinism do they have? Third, can they be evaluated tractably under simple path and trail semantics?

Concerning the first question, we show that all the RPQs can be classified in only 572 different syntactic shapes, which we provide in a downloadable data set in Zenodo. Furthermore, we classify the the relative use of various RPQ operators, and popular predicates that are used for transitive navigation. Concerning the second question, we show that although non-determinism occurs in the RPQs, less than one in ten million requires a deterministic finite automaton with more states than the size of the regular expression. This is remarkable because this blow-up is known to be exponential in the worst case.

When using this data set, please cite the following paper:
@inproceedings{HM25,
author = {Janik Hammerer and Wim Martens},
title = {A Compendium of Regular Expression Shapes in SPARQL Queries},
booktitle = {Joint International Workshop on Graph Data Management Experiences {\&} Systems {(GRADES)}
and Network Data Analytics (NDA)},
publisher = {{ACM}},
year = {2025},
url = {https://doi.org/10.1145/3735546.3735853},
doi = {10.1145/3735546.3735853}
}

Workflow Task Graph Dataset

This dataset contains three sets of task graphs representing different types of task workflows:

• Elementary - contains trivial graph shapes, such as tasks with no dependencies or simple fork-join graphs. This set should test how the scheduler heuristics react to basic graph scenarios that frequently form parts of larger workflows.
• IRW - is inspired by real-world workflows, such as machine learning cross-validation or map-reduce.
• Pegasus - is derived from graphs created by Pegasus Synthetic Workflow Generators (https://github.com/pegasus-isi/WorkflowGenerator)

All of the provided task graphs are generated and compatible with ESTEE (https://github.com/It4innovations/estee) that allows to simulate their execution on a distributed system using various scheduling heuristics and environment conditions.

Data Format

Task graphs are stored in {elementary, irw, pegasus}.zip files that contain JSON representation of respective task graphs with the following fields:

• `graph_name` - Task graph name
• `graph_id` - Unique task graph identifier
• `graph` - Task graph representation - list of tasks where each task is represented as a dictionary with the following keys:
• `d`: Actual task duration in seconds (float value)
• `e_d`: User estimated task duration in seconds (float value)
• `cpus`: Task CPU core requirements (integer value)
• `outputs`: List of task outputs (list of integers indicating sizes of task outputs in MiB)
• `inputs`: List of task inputs in format of list [task\_id, output\_index]}. Output index is zero-based.

For example this task graph:

[{'d': 200, 'e_d': 180, 'cpus': 1, 'outputs': [100], 'inputs': []},

{'d': 50, 'e_d': 60, 'cpus': 2, 'outputs': [], 'inputs': [[0, 0]]}]

contains two tasks. One requiring no input, single CPU core with estimated duration 180s, actual duration 200s and producing a single output of 100 MiB. And another one requiring as an input task0's 0-th output, requiring 2 CPU cores, producing no output with estimated duration 60s and actual duration 50s.

Parsing the data

In Python, to load the elementary task graph set run the following snippet:

import pandas as pd

graphs = pd.read_json("./elementary.zip")

If you have Estee installed, you can use its provided `json_deserialize`

function to parse the JSON encoded graphs into Estee TaskGraph data structure.

from estee.serialization.dask_json import json_deserialize

graph_json = graphs.loc[0, "graph"]

graph = json_deserialize(graph)

This resource is Astrea-KG, a Knowledge Graph that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraints patterns.

Italy Exports of monofilament, rods, sticks, profile shapes of plastics to Azerbaijan was US$199.95 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Italy Exports of monofilament, rods, sticks, profile shapes of plastics to Azerbaijan - data, historical chart and statistics - was last updated on July of 2025.

Portugal Exports of monofilament, rods, sticks, profile shapes of plastics to Mexico was US$306.99 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Portugal Exports of monofilament, rods, sticks, profile shapes of plastics to Mexico - data, historical chart and statistics - was last updated on August of 2025.

Kazakhstan Imports from Belgium of Densified wood blocks/plates/strips/profile shapes was US$9 during 2011, according to the United Nations COMTRADE database on international trade. Kazakhstan Imports from Belgium of Densified wood blocks/plates/strips/profile shapes - data, historical chart and statistics - was last updated on July of 2025.

Slovakia Imports from Austria of Densified wood blocks/plates/strips/profile shapes was US$56 during 2024, according to the United Nations COMTRADE database on international trade. Slovakia Imports from Austria of Densified wood blocks/plates/strips/profile shapes - data, historical chart and statistics - was last updated on July of 2025.

Laos Imports of monofilament, rods, sticks, profile shapes of plastics from China was US$319.98 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Laos Imports of monofilament, rods, sticks, profile shapes of plastics from China - data, historical chart and statistics - was last updated on July of 2025.

Latvia Exports of monofilament, rods, sticks, profile shapes of plastics to Uzbekistan was US$69 during 2022, according to the United Nations COMTRADE database on international trade. Latvia Exports of monofilament, rods, sticks, profile shapes of plastics to Uzbekistan - data, historical chart and statistics - was last updated on August of 2025.

Denmark Imports from Estonia of Densified wood blocks/plates/strips/profile shapes was US$52.95 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Denmark Imports from Estonia of Densified wood blocks/plates/strips/profile shapes - data, historical chart and statistics - was last updated on July of 2025.

Latvia Exports of monofilament, rods, sticks, profile shapes of plastics to Austria was US$369 during 2024, according to the United Nations COMTRADE database on international trade. Latvia Exports of monofilament, rods, sticks, profile shapes of plastics to Austria - data, historical chart and statistics - was last updated on July of 2025.

Italy Imports from Romania of Densified wood blocks/plates/strips/profile shapes was US$57.98 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Italy Imports from Romania of Densified wood blocks/plates/strips/profile shapes - data, historical chart and statistics - was last updated on July of 2025.

Guatemala Exports of monofilament, rods, sticks, profile shapes of plastics to Mexico was US$189.28 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Guatemala Exports of monofilament, rods, sticks, profile shapes of plastics to Mexico - data, historical chart and statistics - was last updated on July of 2025.

Turkey Exports of monofilament, rods, sticks, profile shapes of plastics to South Korea was US$272.07 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Turkey Exports of monofilament, rods, sticks, profile shapes of plastics to South Korea - data, historical chart and statistics - was last updated on August of 2025.

Austria Exports of monofilament, rods, sticks, profile shapes of plastics to Kazakhstan was US$60.99 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Austria Exports of monofilament, rods, sticks, profile shapes of plastics to Kazakhstan - data, historical chart and statistics - was last updated on July of 2025.