Find research datasets worth reusing

Pylint Results for Python Code Snippets on Stack Overflow

This dataset contains valid pylint results for all Stack Overflow code snippets from SOTorrent that meet the following criteria <ul> <li>Tagged with 'python'</li> <li>6 lines and above</li> <li>Contains basic python syntax (i.e. 'print', 'import', '(', '=')</li> <li>Produces a result when processed by Pylint</li> </ul>

Reduction Results for an Artificial Fish Tail

Computation Results for an extensive comparison of model reductions methods for structure preserving reduction of second order mechanical systems applied to an artificial fishtail model.

What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results

This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings. Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website. It is a tab-separated file with the following attributes: ID: Unique row identifier ID Query: Unique search query identifier Query: German search query     Position: Result position to the search query     URL: URL of the search result     Host: Host of the search result    Company: Name of the company on the website Street: Street in the address on the website     Zipcode: Street in the address on the website          Location: Location in the address on the website        District: District in the address on the website             State: State in the address on the website                 Country: Country in the address on the website

Results from "Binary Reduction of Dependency Graphs"

The raw data of the reduction of the 238 bugs as extracted from the runs of the four algorithms ddmin, verify, closure, binary. An extra column also exists that describes the run of Binary Reduction on the list of classes directly. The columns in `deliverable.csv` are predicate (the name of the decompiler), name (the name of the project we ran on), size (number of classes), scc (number of strongly connected components). Then for each tool we have size, scc (final size and scc), iters (iterations to last success), total-iters (iterations before finishing), time (time to last success (s)), total-time (time before finishing), timeout (did the predicate timeout, or succeed), check (did the bug still exist after reduction) The `benchmarks.csv` covers basic statistics about the programs used in the results. Lib is the number of library classes, LOC is lines of source code in the program, classes are the number of classes, edges are the edges in the dependency graph, degree is the average in and out degree in the graph, scc is the number of strongly connected components, out_degree and in_degree is the median in and out degree in the graph.

Data to reproduce FAME results

Synthetic WGBS data set with ground truth and processed EPIC bead data set to reproduce results obtained for FAME and other methods appearing in [coming soon]. The original WGBS data complementing the EPIC bead calls are published by Pidsley et al. and should be downloaded using the IDs provided in the Online Appendix [coming soon].

opencc-by-4.0Mar 2019View details →

First Results from ABRACADABRA-10 cm: A Search for Sub-$\mu$eV Axion Dark Matter — 2018 Data

Extracted limits from the Axion Dark Matter search associated with the article: First Results from ABRACADABRA-10 cm: A Search for Sub-μeV Axion Dark Matter (To be published in Phys. Rev. Lett. 2019)

Results of TIMES model & inputs and outputs of the multicriteria and portfolio analysis

These datasets contain the underlying data for the following publication: Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, https://doi.org/10.1016/j.energy.2018.12.180

Task Scheduler Performance Survey Results

Task scheduler performance survey This dataset contains results of task graph scheduler performance survey. The results are stored in the following files, which correspond to simulations performed on  the `elementary`, `irw` and `pegasus` task graph datasets published at https://doi.org/10.5281/zenodo.2630384. <ul> <li>elementary-result.zip</li> <li>irw-result.zip</li> <li>pegasus-result.zip</li> </ul> The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code: ```python import pandas as pd frame = pd.read_csv("elementary-result.zip") ``` Each row in the frame corresponds to a single instance of a task graph that was simulated with a specific configuration (network model, scheduler etc.). The list below summarizes the meaning of the individual columns. <ul> <li>graph_name - name of the benchmarked task graph</li> <li>graph_set - name of the task graph dataset from which the graph originates</li> <li>graph_id - unique ID of the graph</li> <li>cluster_name - type of cluster used in this instance the format is <number-of-workers>x<number-of-cores>; 32x16 means 32 workers, each with 16 cores</li> <li>bandwidth - network bandwidth [MiB]</li> <li>netmodel - network model (simple or maxmin)</li> <li>scheduler_name - name of the scheduler</li> <li>imode - information mode</li> <li>min_sched_interval - minimal scheduling delay [s]</li> <li>sched_time - duration of each scheduler invocation [s]</li> <li>time - simulated makespan of the task graph execution [s]</li> <li>execution_time - real duration of all scheduler invocations [s]</li> <li>total_transfer - amount of data transferred amongst workers [MiB]</li> </ul> The file `charts.zip` contains charts obtained by processing the datasets. On the X axis there is always bandwidth in [MiB/s]. There are the following files: <ul> <li>[DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds] </li> <li>[DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration.</li> <li>[DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB]</li> <li>[DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple"</li> <li>[DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB]</li> <li>[DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case</li> <li>[DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode</li> </ul> Reproducing the results 1. Download and install Estee (https://github.com/It4innovations/estee) $ git clone https://github.com/It4innovations/estee $ cd estee $ pip install . 2. Generate task graphs You can either use the provided script `benchmarks/generate.py` to generate graphs from three categories (elementary, irw and pegasus): $ cd benchmarks $ python generate.py elementary.zip elementary $ python generate.py irw.zip irw $ python generate.py pegasus.zip pegasus or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384. 3. Run benchmarks To run a benchmark suite, you should prepare a JSON file describing the benchmark. The file that was used to run experiments from the paper is provided in `benchmark.json`. Then you can run the benchmark using this command: $ python pbs.py compute benchmark.json The benchmark script can be interrupted at any time (for example using Ctrl+C). When interrupted, it will store the computed results to the result file and restore the computation when launched again. 3. Visualizing results $ python view.py --all <result-file> The resulting plots will appear in a folder called `outputs`.

The dataset of covalent bond lengths resulting from the first-principle calculations

Optimized geometries (geoms_b973c_optimized.tar.gz, geoms_pbeh3c_optimized.tar.gz ), sample MP2 single point input files (mp2singlePointOnB3lypInputs.tar.gz) based on initial geometries (B3LYP-optimized as in PubChemQC), JANPA output files (janpa_b973cGeoms.tar.gz, janpa_b3lypGeoms.tar.gz, janpa_pbeh3cGeoms.tar.gz ), and files for the covalent bond lengths dataset: selected_mols.txt - identifiers of molecules with method-independent connectivity, unique InChI and selected as being non-aromatic; numbers of lone pairs (LP) CLPOs for atoms ( LPs_b3lyp,  LPs_B97-3c, LPs_PBEh-3c ); numbers of bonding (BD) CLPOs for atoms ( BDs_b3lyp, BDs_B97-3c, BDs_PBEh-3c ); locBonds_b3lyp.txt , locBonds_b97-3c.txt , locBonds_pbeh-3c.txt - the datasets of the bonds not affected by electron delocalization. Each line in each file has the following format: Element Element BondMultiplicity MoleculeId AtomId1 AtomId2

Experiment Result Plots of Data Stewardship (FAIR - Assignment 1)

These plots show the results of the experiment by creating a scatterplot of the variables and a line plot.

Results of SLIMCAT

SLIMCAT data, EOFs and PCs, obtained from EOF analysis of 1984-2000 and 2000-2015, as well as the results of composite analysis of 1984-2015 data.

opencc-by-4.0May 2019View details →

Socio-economic survey results

Task 7.1 from WP7 required of the collection of socio-economic data from the implementation phase of the pilot measures in the 11 pilot cases in order to assess both impacts. An online survey was distributed among all stakeholders involved in the implementation of measures. With the exception of several open questions, most of the questions had a pre-defined range of answers. Questions were addressed to both management and staff levels. The dataset includes data relate to the following aspects: 1. Social data <ul> <li>Gender</li> <li>Position in organization (management/staff)</li> <li>Work hours</li> <li>Workers</li> <li>Job satisfaction</li> <li>Training</li> <li>Acceptance</li> <li>Complaints from staff and tourists</li> <li>Awareness potential perception</li> <li>Willingness to adopt further actions</li> <li>Gender impact perception on various aspects</li> </ul>   2. Economic data <ul> <li>Labor costs</li> <li>Equipment costs</li> <li>Material costs</li> <li>Economic savings</li> </ul>   The results of the data served as support for the evaluation of the socio-economic and gender impacts included in D7.1 of URBAN-WASTE.

opencc-by-4.0Jun 2019View details →

Yerrida Basin 3D geological model and gravity inversion results

This dataset contains an archive for an implicit 3D geological model of the Yerrida Basin, southern Capricorn region, Western Australia. Yerrida_Basin_3D.zip is a GeoModeller three dimensional geological model. Also included are 2D and 3D voxets resulting from inversion of gravity data using the geological model as a constraint. Geomodeller software is available from here: <a href="https://www.intrepid-geophysics.com/ig/index.php?page=downloads">https://www.intrepid-geophysics.com/ig/index.php?page=downloads</a> This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin. Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.

openbsd-3-clauseJul 2019View details →

Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results

Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results   This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019.  [1, 2]. The data files contents and format are described.   Each data file is stored in HDF5 format written with h5py [3, 4]. The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure: <blockquote> + :|____Author (str [50]) :|____Bias (str [3]) :|____DOI (str [22]) :|____Date (str [14]) :|____Description (str [81]) :|____File_Creator (str [44]) :|____License (str [2140]) |____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))     :|____Description (str [41])     :|____Format (str [94])     :|____Units (str [3]) |____XYZ (h5py._hl.dataset.Dataset (3, 101)) |____phi (h5py._hl.dataset.Dataset (101, 101, 101))   </blockquote> See the metadata within the file for units of each variable.   The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure: <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [32])</pre> <pre>    :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [59])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [59])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [41])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [47])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [37])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [38])</pre> <pre>    :|____Units (str [4])</pre> </blockquote>   The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.   1.         Delzanno, G.L., et al., CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies. IEEE Transactions on Plasma Science, 2013. 41(12): p. 3577-3587. 2.         Meierbachtol, C.S., et al., An electrostatic Particle-In-Cell code on multi-block structured meshes. Journal of Computational Physics, 2017. 350: p. 796-823. 3.         Folk, M., A. Cheng, and K. Yates. HDF5: A file format and I/O library for high performance computing applications. in Proceedings of supercomputing. 1999. 4.         Collette, A., et al., h5py/h5py 2.9.0. 2018.

Myotonic dystrophy research: imaging result files ( DTI, VBM) from a 5-year longitudinal follow-up study

This ZIP-file contains supplementary data belonging to the manuscript "Tracking the brain in myotonic dystrophy: a 5-year longitudinal follow-up study", published in PLOS ONE. In this manuscript we aimed to examine the natural history of brain involvement in adult-onset myotonic dystrophies type 1 and 2 (DM1, DM2). We conducted a longitudinal observational study to examine functional and structural cerebral changes in myotonic dystrophies. We enrolled 16 adult-onset DM1 patients, 16 DM2 patients, and 17 controls. At baseline (T1) and at follow-up (T2) participants underwent neurological, neuropsychological, and 3T-brain MRI examinations using identical study protocols that included voxel-based morphometry and diffusion tensor imaging. The ZIP-file contains imaging result files from the different statistical analyses.

opencc-by-4.0Jul 2019View details →

Experimental Results of the UNIC OC4 FED4FIRE+ Project

This repository contains the detailed experimental results of the Unikernel-based CDNs for 5G Networks (UNIC) project. This work has received funding from the EU's Horizon 2020 research and innovation programme through the 4th open call scheme of the FED4FIRE+ (grant agr. no 732638).

opencc-by-4.0Aug 2019View details →

FlowBlaze on Fed4FIRE+ FB-FIRE Final Results of the Experiment

This repository contains the results of the FB-FIRE experiment. The aim of FB-FIRE is to test, validate and assess in the Fed4FIRE+ Virtual Wall testbed the performance of FlowBlaze, a novel stateful programmable dataplane engine that can simplify network functions (NF) design and implementation, while providing high performance packet forwarding, and support for future hardware offloading to SmartNICs. FlowBlaze is an abstraction that can be implemented in an efficient DPDK-accelerated software engine, conceived to rapidly design and deploy complex network functions hiding the complexity of software optimizations associated to the development of network functions.

opencc-by-4.0May 2019View details →

Results of user research project to understand data curation practices

Supporting scalable curation is a part of the mission of the Elixir Data Platform.Thus far, we have established infrastructure capable of ingesting and aggregating text-mined outputs from multiple providers and making these available via an API. This public API is used by Europe PMC to display specific entities and relationships on full text articles (via the SciLite application). To ensure that the future development of this infrastructure meets the needs of curators, we first carried out user research to understand and identify common workflow patterns and practices via an observational study. Building on these outcomes, we then devised a curator community survey to more specifically understand which entity types, sections of a paper and tools are of top priority to address. The results of the project is presented here.

opencc-by-4.0Sep 2019View details →

data set related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms

This record contains raw data related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms