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opencc-by-4.0Oct 2024View details →

Artifacts for paper "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge" submitted to TOSEM

The project includes the data and code used in the submitted TOSEM paper titled "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge"

E.A.T. Knowledge Graph Project

The E.A.T. Knowledge Graph Project centers on the U.S. avant-garde movement Experiments in Art and Technology (E.A.T.). Data was sourced from documents from the Robert Rauschenberg Foundation Archives, a published bibliography by E.A.T. co-founder Billy Klüver, and edge-notched cards from the Getty Research Institute. Processed with custom tools, allowing for semantic encoding of statements through "painting triples”, the data is stored in and accessible through a Wikibase knowledgebase. This approach aims to reduce barriers to creating knowledge graphs and expand reuse opportunities for research, applications, and data integration in cultural heritage contexts. The Semantic Lab Wikibase is available at <a href="https://base.semlab.io/wiki" target="_blank" rel="noopener">https://base.semlab.io/wiki </a>and on GitHub at <a href="https://github.com/SemanticLab/data-export" target="_blank" rel="noopener">https://github.com/SemanticLab/data-export.</a> Learn more about the Semantic Lab at <a href="https://semlab.io/" target="_blank" rel="noopener">https://semlab.io/. </a>

SHY Project - Baseline Lifecycle Impacts Dataset

This document has been developed by Julia F. Chozas, Consulting Engineer and Wavepiston A/S. The aim of this document is to present the database compiled as part of Task 6.1 of the SHY project.  These data has also been presented in Deliverable 6.1 "Baseline Lifecycle Impacts", which will be publicly available at: https://shyproject.eu/project-results/ Any questions can be directly addressed to Julia Fernandez Chozas (info@juliafchozas.com)

Project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model"

<pre>README file to the project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model" October 8, 2024 Authors: Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Lorenzo Rovigatti, Luca Tubiana, Samuela Pasquali, Raffaello Potestio</pre> ================================== Trajectories and obsvervables relative to the scientific paper entitled: "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with oxRNA" Authors: Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Luca Tubiana, Samuela Pasquali, Raffaello Potestio Brief description of the content of this folder: |_ oxDNA-CCMV_force.zip: the modified version of the oxDNA software we used to make the simulations with the CCMV-like spherical potential described in the paper | |_ CCMV_RNA2_oxRNA_simulations    |_ FOLDING (files of the "freely-folding" simulations)    |   |_ FIRST_LAST_FRAMES (frames generated by oxDNA software)    |   |   |_ 0.15M (first and last frames of the runs at 0.15M)    |   |   |   |_ REP1 (files relative to REP1 / REPI)    |   |   |   |   |_ first.dat    |   |   |   |   |_ last.dat     |   |   |   |    |   |   |   |_ REP2 (files relative to REP2 / REPII)    |   |   |   |   |_ first.dat    |   |   |   |   |_ last.dat     |   |   |   |    |   |   |   |_ REP3 (files relative to REP3 / REPIII)    |   |   |       |_ first.dat    |   |   |       |_ last.dat     |   |   |    |   |   |_ 0.50M (first and last frames of the runs at 0.50M)    |   |   |  |_ [same structure as for 0.15M]    |   |   |    |   |   |_ 0.50M (first and last frames of the runs at 0.50M, T293K)    |   |   |  |_ [same structure as for 0.15M]    |   |   |    |   |   |_ 0.50M (first and last frames of the runs at 0.50M, T293K, harmonic constraint to the ends)    |   |      |_ [same structure as for 0.15M]    |   |    |   |    |   |_ HB_FILES (base pairs files generated by oxDNA software)    |   |   |_ hb_015M_I.dat    (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.15M salt concentration)    |   |   |_ hb_015M_II.dat    (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.15M salt concentration)    |   |   |_ hb_015M_III.dat    (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.15M salt concentration)    |   |   |_ hb_050M_I.dat    (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration)    |   |   |_ hb_050M_II.dat    (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration)    |   |   |_ hb_050M_III.dat    (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration)    |   |   |_ hb_050M_293K_I.dat    (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K)    |   |   |_ hb_050M_293K_II.dat    (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K)    |   |   |_ hb_050M_293K_III.dat    (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K)    |   |   |_ hb_050M_293K_harm_I.dat    (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)    |   |   |_ hb_050M_293K_harm_II.dat    (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)    |   |   |_ hb_050M_293K_harm_III.dat    (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)    |   |    |   |_ example_input.ox (example of oxDNA input file used to launch simulations and dump observables and trajectories)    |   |    |   |_ example_input_harm.ox (example of oxDNA input file used to launch simulations with harmonic constraint to the ends and dump observables and trajectories)    |   |    |   |_ harmonic_ends.dat (constraint force file used to run simulations with harmonic constraint to the ends)    |    |_ PACKED        |_ SIM_VTHEO (files of the simulations with the analytic-based external potential, as described in the paper...)        |   |_ 0.15M (... with a salt concentration of 0.15M)        |   |   |_ REP1 (Replica with id 1, or I)        |   |   |   |_ MD_scripts (files required by oxDNA software to launch the simulation for this replica)        |   |   |   |   |_ input_0.15M_REP1.ox (input file of oxDNA software to launch the simulation)        |   |   |   |   |_ force_0.15M_REP1.dat (additional file required for the application of an external force in the simulation, containing the relative parameters)        |   |   |   |        |   |   |   |_ input_structure (structure files in oxDNA format, one of which is used as starting configuration for the specific replica)        |   |   |   |   |_ PackingRNA2_0.15M_REP1.dat (last structure of simulation with time-dependent external packaging force)        |   |   |   |   |_ PackingRNA2_0.15M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)        |   |   |   |   |_ PackingRNA2_0.15M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)        |   |   |   |   |_ PackingRNA2_0.5M_REP1.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)        |   |   |   |   |_ PackingRNA2_0.5M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)        |   |   |   |   |_ PackingRNA2_0.5M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)        |   |   |   |        |   |   |   |_ data (output data generated by the simulation)        |   |   |       |_ 1.E (files of the observables dumped during simulation)        |   |   |       |   |_ E_RNA2_0.15M_REP1.dat  (energies of the system per each time frame: time frame, potential energy U, kinetic energy, total energy)        |   |   |       |   |_ P_RNA2_0.15M_REP1.dat  (internal pressure of the system: time frame, total pressure, stress tensor components xx, yy, zz, xy, xz, yz)        |   |   |       |   |_ F_RNA2_0.15M_REP1.dat  (components of the external force acting on each nucleotide, ordered according to the topology, per each frame)        |   |   |       |   |_ HB_RNA2_0.15M_REP1.dat (base pairs annotated, per each time frame dumped)        |   |   |       |        |   |   |       |_ 3.restart (restart/last configuration file dumped during simulation)        |   |   |           |_ last_conf_RNA2_0.15M_Yukawa_REP1.dat (last frame dumped in the simulation, in oxDNA format)        |   |   |        |   |   |        |   |   |_ REP2 (Replica with id 2, or II)        |   |   |   |_ [same content as REP1, but relative to replica 2 or II]        |   |   |        |   |   |_ REP3 (Replica with id 3, or III)        |   |       |_ [same content as REP1, but relative to replica 3 or III]        |   |        |   |_ 0.5M  (... with a salt concentration of 0.5M)        |   |   |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]        |   |        |   |_ 0.5M_293K_harm  (... with a salt concentration of 0.5M, Temperature 293K and harmonic constraint to the ends)        |       |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]        |        |_ SIM_VCCMV (files of the simulations with the structure-based external potential, as described in the paper)            |_ [same content as SIM_VTHEO, but for simulations done with the structure-based external potential] For any additional file or data not present here, that was used to produce results in the paper, please ask to: giovanni.mattiotti@inserm.fr, manuel.micheloni@unitn.it, lorenzo.petrolli@unitn.it

opencc-by-4.0Oct 2023View details →

Simulated sPHENIX Time-Projection Chamber (TPC) Data in Central Au-Au Collisions at sqrt[s] = 200 GeV, outer layer group

This is the dataset we used to train the 2D and 3D Bicephalous Convolutional Autoencoders (BCAEs) described in "Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data" published in the 9th International Workshop on Data Analysis and Reduction for Big Scientific Data (https://drbsd.github.io/).  To untar the file, run `tar -xvzf outer.tgz`.  The Time Projection Chamber (TPC) is a hollow cylinder.  Along the radial dimension, the TPC is composed of 48 cylindrical layers of small sensors, which are grouped into three layer groups: inner, middle, and outer. Each layer group has 16 consecutive layers. On each TPC layer, the voxels are presented as a rectangular grid with rows along the z (or horizontal) direction and columns along the azimuthal direction. Within one layer group, all layers have the same number of rows and columns. This allows us to represent the ADC values from one layer group as a 3D array.The data released here focuses on the outer layer group, where the array of ADC values has shape (16, 2304, 498) in the radial, azimuthal, and horizontal orders.  The full voxel data are divided into 24 equal-size non-overlapping sections: 12 along the azimuthal direction (30 degrees per section) and 2 along the horizontal direction (divided by the transverse plane passing the collision point). We call one such section a TPC wedge. The array of ADC values from each TPC wedge in the outer layer has shape (16, 192, 249), listed in radial, azimuthal, and horizontal directions, respectively. The TPC wedges are used as the direct input to the deep neural network compression algorithms.We simulated 1310 events for central sqrt[s]=200 GeV Au-Au collisions with 170kHz pile-up. The data were generated with the HIJING event generator and Geant4 Monte Carlo detector simulation package integrated with the sPHENIX software framework.The simulated TPC readout (ADC values) from these events are represented in a 10-bit unsigned integer in [0, 1023]. To reduce unnecessary data transmission between detector pixels and front-end electronics, a zero-suppression algorithm has been applied. ADC values below 64 are suppressed to zero as most of them are noise. The zero compression makes the TPC data sparse at about 10% nonzero occupancy. We divide the 1310 total events into 1048 events for training and 262 for testing. Each event contains 24 outer-layer wedges. Thus, the training partition contains 25152 TPC outer-layer wedges, while the testing portion has 6288 wedges. The compression algorithm compresses each wedge independently. The dataset has the following structure:<ol><li>24 subfolders with the name `12-2_[azimuthal section]-[horizontal section]` where the [azimuthal section] is labeled by an integer in [0, 11] and the [horizontal section] is labeled by either 0 or 1. Each file in one of the subfolders has the name in the format "AuAu200_170kHz_10C_Iter2_[simulation id].xml_TPCMLDataInterface_[event id within simulation].npy". There are 131 simulations, and each simulation contains 10 independent events (and hence the 1310 total events as mentioned above). Each [event id within simulation] is an integer in [0, 9].</li><li>train.txt: a list of all TPC wedges for the training split.</li><li>text.txt: a list of all TPC wedges for the test split.</li></ol>Note that the dataset is split by events. That is, if a TPC wedge from an event is in the train split, all 24 wedges from the same event will all be in the train split. The same holds for the test split.

opencc-by-4.0Sep 2024View details →

CAMI2 Challenge - Human Microbiome Project Toy Database - sample 19 - regenerated using recent RefSeq representative genomes

Open the record for dataset details and reuse information.

Auxiliary and validation data for Project SaGEA

Auxiliary and validation data for Project SaGEA (Error Assessment for Satellite Gravity) at <a href="https://github.com/NCSGgroup/SaGEA" target="_blank" rel="noopener">https://github.com/NCSGgroup/SaGEA</a>.

opencc-by-4.0Jun 2021View details →

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the 21st century in Europe. Here we present a set of raster files that contain discrete zones of climatic stability and instability, also referred to as hotspots and coldspots. Our analysis of future climatic stability was based on projected changes of nine climate variables selected to proxy key aspects of terrestrial ecosystem dynamics. The underlying climate projection, including different indices, are available at <a href="https://zenodo.org/record/3952159#.YNWN_OgzaUl">https://zenodo.org/record/3952159#.YNWN_OgzaUl</a>. The cumulative change of all nine climate variables, termed Aggregate Climate Change (ACC), is used to identify regions of significantly higher or lower climatic stability (Getis-Ord Gi* p-value < 0.05) across Europe. We carry out this assessment at the continental and regional scales. While the continental assessment reports the large-scale latitudinal and orographic patterns of climatic stability, the regional assessment explores the residual ACC variation that remained after extracting the large-scale continental trend. The raster files present climatic features that were identified to persist in their location throughout the 21st century (i.e., in periods 2021-2040, 2041-2060, and 2061-2100) and were supported by a majority of climate projections considered in this study. Specifically, we considered five GCM-RCM pairs driven by two RCP scenarios, RCP4.5 and RCP8.5. The targeted areas of climatic stability were required to be confirmed by four out of five climate models nested within each RCP, at least. The used naming convention is as follows: <ul> <li>Scale – Global, Regional</li> <li>Type of the zone – HotSpot (zone of significantly low climatic stability), ColdSpot (zone of significantly high climatic stability)</li> <li>Driving RCP scenario – RCP45, RCP85</li> </ul> Raster files with RCP indicated in their name refer to either RCP4.5 or RCP8.5. These files contain two values referring to the presence (1) or absence (0) of climatic stability areas: Global_HotSpots _RCP45 Global_ColdSpots _RCP45 Regional_HotSpots _RCP45 Regional_ColdSpots _RCP45 Global_HotSpots _RCP85 Global_ColdSpots _RCP85 Regional_HotSpots _RCP85 Regional_ColdSpots _RCP85 Raster files without RCP code in their name contain combinations of both RCPs. The cell values indicate absence (0), presence in RCP4.5 OR RCP8.5 (1), or presence in RCP4.5 AND RCP8.5 (2) of climatic stability areas: Global_HotSpots Global_ColdSpots Regional_HotSpots Regional_ColdSpots The format of raster files is tiff. All data are in the GCS_WGS_1984 geographic coordinate system.

dryad32/100

Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project

Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machi ne learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.

opencc-zeroDec 2017View details →

opencc-by-4.0Dec 2020View details →

Database of frequency-domain terahertz reflection spectra for the DETRIS project

A detailed description of the database can be found in the Instructions file.

opencc-by-4.0Jul 2021View details →

Low Density EEG Files from Bucharest Early Intervention Project for HAPPILEE Software

The various steps of the HAPPILEE automated pipeline (for low-density electroencephalography (EEG)) were optimized using a subset of developmental EEG files from the Bucharest Early Intervention Project (BEIP) (for full study design, see Zeanah et al., 2003). The optimization dataset includes resting-state EEG from three groups of children living in Romania starting in 2001. The first group, referred to as the Care as Usual Group (CAUG), is composed of children living across six institutionalized care facilities throughout Bucharest, Romania. The second group is the Foster Care Group (FCG), which is composed of children who were removed from these institutions through random assignment and placed in a foster care intervention. The final group is the control group (CG), made up of a community sample of children living with their biological families who have never been placed in institutionalized care or foster care. We selected a subset of thirty EEG files across the three groups from the greater dataset (CAUG, n=8; FCG, n=8, CG, n=14). The average age at the start of the baseline assessment across the three groups was 17.40 months, with a range of 6.28 - 29.98 months (averages per group: CAUG=18.30; FCG=16.21; CG=17.58). The resting-state EEG for all children was recorded with the James Long system from twelve scalp sites (F3, F4, Fz, C3, C4, P3, P4, Pz, O1, O2, T7 and T8) using a lycra Electro-Cap (Electro-Cap International Inc., Eaton, OH) with sewn-in tin electrodes. The thirty full-length files from the BEIP EEG dataset are titled A.set, B.set, etc. There are three 30-second EEG segments per subject (45 total) with the following conditions: The _artifact_laden files have data with heavy amounts of artifact, the _clean.set files have data determined to be generally clean and without considerable artifact, and the _artifact_added.set files contain the clean 30-second data segments with artifacts taken from the same subject added to the clean data. To create the _artifact_added.set files, we isolated artifact timeseries by running ICA on the artifact laden 30-second files and selecting approximately 2 components that were determined to be artifact with minimal neural data via visual inspection and automated classification through both the ICLabel and Multiple Artifact Rejection Algorithm options. We subsequently added those artifact component timeseries on top of the clean 30-second data segment from the same individual. These three types of files were used to evaluate the HAPPILEE pipeline performance for artifact removal. The simulated_data.set EEG data was created with code from Bridwell et al. (2018). In short, the simulated EEG consisted of four signals. The four signals had distinct spatial patterns and frequency ranges (1.00-3.91Hz, 3.91-7.81Hz, 7.81-15.62Hz, and 15.62-31.25Hz). For a more thorough description on how the simulated signals were created, see Bridwell et al., 2018. The simulated data is high-density EEG, but a subset of channels was selected for analyses to mimic low-density data constraints. The simulated_signal_with_artifact.set contains the simulated EEG data with blink and muscle artifact added. To get the blink artifact, we used a clear blink independent component (IC) from an adult participant (see Leach et al., 2020 for the specific study details). This IC was selected based on both an automated artifactual IC detection algorithm and visual inspection by two researchers with over five years of EEG and at least two years of ICA experience. For the muscle artifact, we pulled eight muscle ICs from the BEIP dataset used above in the artifact addition approach. After adding the artifact to the simulated EEG data, we did a 1Hz highpass and 35 Hz lowpass filter. Following this, we epoched the data into two-second epochs (50% overlap) to prepare the data for wavelet thresholding and/or artifact rejection. For artifact rejection, we used a -100 to 100 μV voltage threshold to identify bad epochs. We also required both frontal electrodes to exceed this threshold in order to classify an epoch as containing a blink. The Bucharest Early Intervention Project was carried out in accordance with the recommendations of the Institutional Review Boards at Boston Children’s Hospital, University of Maryland, and Tulane University. Moreover, informed consent was signed by the Commissioner for Child Protection for each child participant living in his sector of Bucharest, as dictated by Romanian law. Further assent for each procedure was obtained from each caregiver who accompanied the child to the visit. All files here have been deidentified.

opencc-by-nc-4.0Jul 2021View details →

CURSUS An Online Resource of Medieval Liturgical Texts -- project data files and website

This is a legacy deposit of the data and website of the CURUS Project. An Online Resource of Medieval Liturgical Texts funded by the AHRB from 2000-2003.  This file was uploaded to ZENODO on 2021-07-11 by Dr James Cummings to ensure that there are lots of copies of the CURSUS materials around for the future. A copy is also available at https://github.com/jamescummings/cursus and will be deposited in FigShare as well. Original Creators: Dr James Cummings, Professor David Chadd, Dr Richard Lewis License: Creative Commons, Attribution, Non-Commericial 4.0 International   It includes the TEI P4 XML based markup (with significant project additions documented in the DTD), transformations XSLT 1.0 Stylesheets and an HTML scrape of the website as of 2020-07-11. It includes additional material to the 12 manuscript editions such as a corpus of antiphons/responds/prayers of the Corpus Antiphonalium Officii, and a vulgate bible. The website stopped having new additions in 2009 at the time of Professor David Chadd's sad death.

FIGURE 5 in The Tree of Life Web Project*

FIGURE 5. First sections of some articles and notes. From top to bottom, they are: "Fabaceae Fruits" (Wojciechowski & Mahn, 2006), attached to the Fabaceae page; "Cephalopod Gills" (Young & Vecchione, 2006), attached to the cephalopod page; "Tyrannosaurid Systematics" (Holtz, 2000), attached to the Tyrannosauridae page.

FIGURE 2 in The Tree of Life Web Project*

FIGURE 2. Parts of two typical sections on a ToL branch page, the Characteristics section and the Discussion of Phylogenetic Relationships section.

FIGURE 1 in The Tree of Life Web Project*

FIGURE 1. The start of a branch page in the ToL (Laurin & Gauthier, 1996), on amniotes, showing the navigational center (the tree), links which take one to subgroups or larger groups, and the Introduction.

FIGURE 8 in The Tree of Life Web Project*

FIGURE 8. The glossary in the ToL in use. The user has moved the cursor over the highlighted word "monophyletic" and the definition is being shown.

FIGURE 7 in The Tree of Life Web Project*

FIGURE 7. First sections of some example treehouse pages. From top to bottom, they are: "Nighttime Bug Watching", by Katja Schulz; "Memoirs of a Pacific Treefrog", by a student at Havergal College, a secondary school in Toronto, and "The Study of Fruit Fly Genetics at City High School", by Molly Renner, Kathryn Orzech, and Lisa Schwartz.

FIGURE 6 in The Tree of Life Web Project*

FIGURE 6. Schematic of the Tree of Life Web Project showing the groups best represented, with areas approximately proportional to number of pages, and darkness approximately proportional to fraction of pages that are completed.

FIGURE 4 in The Tree of Life Web Project*

FIGURE 4. First sections of some example leaf pages. From top to bottom, they are: Atlantoxerus getulus (Steppan & Hamm, 2006), Atlanta californiensis (Seapy, 2005), Scyphostegia borneensis (Alford, 2007), and Cranchia scabra (Young & Mangold, 2007).

FIGURE 3 in The Tree of Life Web Project*

FIGURE 3. First sections of some example branch pages. From top to bottom, they are: Annelida (Rouse et al., 2002); Angiosperms (Soltis et al., 2005), Agaricomycotina (Hibbett, 2007a), and Heliconiini (Beltrán et al., 2007).