Find research datasets worth reusing

opencc-by-sa-4.0Mar 2023View details →

unarXive: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network (open subset)

<h2>Description</h2>unarXive is a scholarly data set containing publications' structured full-text, annotated in-text citations, linked non-text content (mathematical notation, figure/table captions) and a citation network.The data is generated from all LaTeX sources on <a href="https://arxiv.org/">arXiv</a> and therefore of higher quality than data generated from PDF files.Typical uses are<ul><li>Training of ML models (citation recommendation, summarization, LLMs)</li><li>Citation context analysis</li><li>Bibliographic analyses</li></ul><h2>Access</h2>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃  <a href="https://github.com/IllDepence/unarXive/raw/master/doc/unarXive_data_sample.tar.gz">D O W N L O A D   S A M P L E</a>   ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛Regarding the full data set, please note the following:<blockquote>Note: this Zenodo record is the "open subset" of unarXive, which contains all permissively licensed papers from arXiv.org. You can find the <a href="https://doi.org/10.5281/zenodo.7752754">full version here</a>.</blockquote>The code used for generating the data set is <a href="https://github.com/IllDepence/unarXive">publicly available</a>.

Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation

1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction. 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.

Marmoset Functional Maps, "A Vocalization-Processing Network in Marmosets"

Marmoset data and functional maps used in the article "A vocalization-processing network in marmosets". (Jafari et al., 2023)

Process controlling iron-manganese regulation of the Southern Ocean biological carbon pump

Netcdf output files at the end of 100-year run Fe-Mn co-limitation model experiments on the ORCA2 grid, as described and discussed in Anugerahanti, et al., in Process controlling iron-manganese regulation of the Southern Ocean biological carbon pump, Philosophical Transactions of the Royal Society A, 2022

Supplementary materials to: "SilicH2O: a graphical user interface for processing silicate glass Raman spectra and quantifying H2O"

The dataset contains: <ul> <li>supplementary text and figures as a single pdf</li> <li>Raman and compositional data in two separate excel files</li> </ul>

opencc-zeroApr 2023View details →

dryad36/100

The Cauchy Process on phylogenies: a tractable model for pulsed evolution

Phylogenetic comparative methods use random processes, such as the Brownian Motion, to model the evolution of continuous traits on phylogenetic trees. Growing evidence for non-gradual evolution motivated the development of complex models, often based on Lévy processes. However, their statistical inference is computationally intensive and currently relies on approximations, high dimensional sampling, or numerical integration. We consider here the Cauchy Process (CP), a particular pure-jump Lévy process in which the trait increment along each branch follows a centered Cauchy distribution with a dispersion proportional to its length. In this work, we derive an exact algorithm to compute both the joint probability density of the tip trait values of a phylogeny under a CP and the ancestral trait values and branch increments posterior densities in quadratic time. A simulation study shows that the CP generates patterns in comparative data that are distinct from any Gaussian process and that Restricted Maximum Likelihood (REML) parameter estimates and root trait reconstruction are unbiased and accurate for trees with 200 tips or less. The CP has only two parameters but is rich enough to capture complex pulsed evolution. It can reconstruct posterior ancestral trait distributions that are multimodal, reflecting the uncertainty associated with the inference of the evolutionary history of a trait from extant taxa only. Applied to empirical datasets taken from the Evolutionary Ecology and Virology literature, the CP suggests nuanced scenarios for the body size evolution of Greater Antilles Lizards and for the geographical spread of the West Nile Virus epidemics in North America, both consistent with previous studies using more complex models. The method is efficiently implemented in C with an R interface in package cauphy, which is open source and freely available online.

EMD data for the paper "Impact of ad-hoc post-processing parameters on the lubricant viscosity calculated with equilibrium molecular dynamics simulations"

This archive contains the post-processing data obtained from EMD simulations of 2,2,4-Trimethylhexane lubricant molecule under various operating conditions. The EMD simulations were performed using LAMMPS with COMPASS force field. (See manuscript and README for details.)

Height covariance correlation function in Contact Process

Data of the correlation function, namely, the height covariance c1(r,t) for 1, 2 and 3 dimensions.  It has been measured for the interface representation of a contact process system at its critical point. See <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a> for more details. Each file contain two headers starting with #. The columns show, in order, the time, the coordinate r, the value of c1(r,t), the error estimate by the jackknife procedure and the numbers of files used. All the columns are separated by space. Data for one-dimensional system in: c1JK-1D-L8192-t4000000-f2000.dat Data for two-dimensional system in: c1JK-2D-L512-t200000-f498.dat Data for three-dimensional system in: c1JK-3D-L128-t40000-f20.dat The datasets have been produced and employed in the context of a scientific work currently published in <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a>.

PDF of rescaled front fluctuations in Contact Process

Data of the PDF of chi (rescaled front fluctuations), for 1, 2 and 3 dimensions. It has been measured for the interface representation of a contact process system at its critical point. See <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a> for more details. Each file contain four headers starting with #. The columns show, in order, the chi, the probability and the error estimate by the jackknife procedure. All the columns are separated by space. Data for one-dimensional system in: chiPDF-1D-L8192-f2000.dat Data for two-dimensional system in: chiPDF-2D-L512-f498.dat Data for three-dimensional system in: chiPDF-3D-L128-f20.dat The datasets have been produced and employed in the context of a scientific work currently published in <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a>.

opencc-by-4.0Dec 2023View details →

Multi-omic integration of DNA methylation and gene expression data reveals molecular vulnerabilities in glioblastoma (processed data)

Glioblastoma multiforme (GBM) is one of the most aggressive types of cancer and exhibits profound genetic and epigenetic heterogeneity, making the development of an effective treatment a major challenge. The recent incorporation of molecular features into the diagnosis of GBM patients has led to an improved categorisation into various tumour subtypes with different prognoses and disease management. In this work, we have exploited the benefits of genome-wide multi-omic approaches to identify potential molecular vulnerabilities existing in GBM patients. Integration of gene expression and DNA methylation data from both bulk GBM and patient-derived GBM stem cell lines has revealed the presence of major sources of GBM variability, pinpointing subtype-specific tumour vulnerabilities amenable to pharmacological interventions. In this sense, inhibition of the AP1, SMAD3 and RUNX1 / RUNX2 pathways, in combination or not with the chemotherapeutic agent temozolomide, led to the subtype-specific impairment of tumour growth, particularly in the context of the aggressive, mesenchymal-like subtype. These results emphasize the involvement of these molecular pathways in the development of GBM and have potential implications for the development of personalized therapeutic approaches.

TRMMPR V8 Level 2A Processed Convective Rain States 1998-2009

This dataset contains Interactive Data Language (IDL) .sav files of Convective Rain States (CRS) from 1998-2009 which are created by k-means clustering on TRMMPR Version 8 Level 2A data. It also contains IDL scripts in the TRMM.zip file that were used to process the raw TRMMPR V8 data into the available CRS data. Details on this clustering can be found in Elsaesser et al. (2010) <a href="https://doi.org/10.1175/2010JCLI3330.1">https://doi.org/10.1175/2010JCLI3330.1</a>.   Readme: - Each line in the seriesFiles txt files points to the directory where the raw downloaded data was originally held. The raw TRMMPR V8 Level 2A data was downloaded from NASA EOSDIS PPS GPM public data archive. - There are two different total number of entries between all the variables in the processed data. This is because some missing values were saved in some variables and not in others. These missing variables are denoted by -1's in the prcid variable (as well as other variables), and can be located and removed under that condition.

TRMMPR and GPM KuPR V8 Level 2A Processed Convective Rain States 2010-2021

This dataset contains Interactive Data Language (IDL) .sav files of Convective Rain States (CRS) from 2010-2021 which are created by k-means clustering on TRMMPR Version 8 and GPM KuPR Version 8 Level 2A data. It also contains IDL scripts in the TRMM.zip and GPM.zip file that were used to process the raw TRMMPR and GPM KuPR V8 data into the available CRS data. This set is part 2 in a continuation of the data available in <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.5281_zenodo.7853386&d=DwMGaQ&c=009klHSCxuh5AI1vNQzSO0KGjl4nbi2Q0M1QLJX9BeE&r=WTOc26e3IBrEp5cDEaVnuU41G2J-MLym-VhzO4aTABg&m=dHNcst9cbr8tlYpnfdrmqG6MCbrWJa8oa0eBAL_XEyqXQY8MPg2RqBoIe8jxqaAd&s=CnET26HiWgS_mV-kbFHpR3iojlRFOREml99vwWXs4Ig&e=">https://doi.org/10.5281/zenodo.7853386</a>. Details on this clustering can be found in Elsaesser et al. (2010) <a href="https://doi.org/10.1175/2010JCLI3330.1">https://doi.org/10.1175/2010JCLI3330.1</a>.   Readme: - Each line in the seriesFiles txt files points to the directory where the raw downloaded data was originally held. The raw TRMMPR and GPM KuPR V8 Level 2A data was downloaded from NASA EOSDIS PPS GPM public data archive. - There are two different total number of entries between all the variables in the processed data. This is because some missing values were saved in some variables and not in others. These missing variables are denoted by -1's in the prcid variable (as well as other variables), and can be located and removed under that condition.

openncgl-uk-2.0Mar 2023View details →

Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022

1- Dataset Summary Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded. 2- Background The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022). 3 – Overview of data collection Table 1 contains the overview of the surveys collected and presented in this repository.                   Table 1. Summary of surveys included in this dataset, by survey date. <table align="center"> <tbody> <tr> <td> Date & Time YYYYMMDD HH:MM </td> <td> Sensor </td> <td> Platform </td> <td> Flight alt. (m asl) </td> <td> Images </td> <td> Surveyed km2 </td> </tr> <tr> <td> 20220803 17:05 </td> <td> A6D </td> <td> TF-203* </td> <td> ~ 850 </td> <td> 46 </td> <td> 4 </td> </tr> <tr> <td> 20220804 11:00 </td> <td> A6D </td> <td> TF-203 </td> <td> ~ 2100 </td> <td> 32 </td> <td> 35 </td> </tr> <tr> <td> 20220813 09:00 </td> <td> A6D </td> <td> TF-203 </td> <td> ~ 750 </td> <td> 123 </td> <td> 9 </td> </tr> <tr> <td> 20220814 13:00 </td> <td> Pléiades </td> <td> PHR1B </td> <td> n/a </td> <td> 2 </td> <td> 14 </td> </tr> <tr> <td> 20220815 08:15 </td> <td> A6D </td> <td> TF-203 </td> <td> 2100 </td> <td> 20 </td> <td> 23 </td> </tr> <tr> <td> 20220816 10:06 </td> <td> A6D </td> <td> TF-203 </td> <td> 2100 </td> <td> 19 </td> <td> 26 </td> </tr> <tr> <td> 20220926 12:00 </td> <td> A6D </td> <td> TF-BMW** </td> <td> 2100 </td> <td> ~20 </td> <td> 18 </td> </tr> </tbody> </table> * TF-203: Savannah S aircraft ** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf. 4- Methods 4.1 Processing of the aerial photographs from 3-16 Aug 2022 Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap. An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine ipfind. This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. 4.2 Processing of the Pléiades stereo images The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of mapproject and parallel_stereo (e.g., Deschamps-Berger et al., 2020). The parallel_stereo routine used default arguments, plus the following arguments: --stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15 We used the DEM from 4 Aug 2022 as the reference for mapproject and for the final DEM co-registration applied to the produced Pléiades DEM. 4.3 Processing of the 26 September 2022 dataset The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022). 4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR) For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.   Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics. <table align="center"> <thead> <tr> <th>  Date Start </th> <th> Date End </th> <th> Time Difference </th> <th> Lava Area* End (km2) </th> <th> dh** (m) </th> <th> Volume+ End (1e+6 m3) </th> <th> TADR++ (m3/s) </th> </tr> </thead> <tbody> <tr> <td> 20220803 13:20 </td> <td> 20220803 17:05 </td> <td> 0d 03h 45m </td> <td> 0.07 </td> <td> 5.88 </td> <td> 0.43 ± 0.03 </td> <td> 32.1 ± 1.5 </td> </tr> <tr> <td> 20220803 17:05 </td> <td> 20220804 11:00 </td> <td> 0d 21h 40m </td> <td> 0.14 </td> <td> 11.13  </td> <td> 1.57 ± 0.05 </td> <td> 17.7 ± 0.8 </td> </tr> <tr> <td> 20220804 11:00 </td> <td> 20220813 09:00 </td> <td> 8d 22h 00m </td> <td> 1.27# </td> <td> 6.90 </td> <td> 10.33 ± 0.6 </td> <td> 11.4 ± 0.7 </td> </tr> <tr> <td> 20220813 13:08 </td> <td> 20220814 13:00 </td> <td> 0d 23h 52m </td> <td> 1.24 </td> <td> 7.28 </td> <td> 10.62 ± 0.70 </td> <td> 2.8 ± 0.8 </td> </tr> <tr> <td> 20220814 13:00 </td> <td> 20220815 08:15 </td> <td> 0d 19h 15m </td> <td> 1.26 </td> <td> 7.46 </td> <td> 10.99 ± 0.55 </td> <td> 4.1 ± 0.8 </td> </tr> <tr> <td> 20220815 08:15 </td> <td> 20220816 10:16 </td> <td> 1d 2h 01m </td> <td> 1.28 </td> <td> 7.49 </td> <td> 11.13 ± 0.53 </td> <td> 2.0 ± 0.7 </td> </tr> <tr> <td> 20220816 10:16 </td> <td> 20220821 06:00## </td> <td> 4d 19h 44m </td> <td> 1.28 </td> <td> 7.69 </td> <td> 11.39 ± 0.44 </td> <td> 0.653 ± 0.10 </td> </tr> </tbody> </table> *Total area of the lava field since 2022-08-03 before activity started. **dh end is the mean thickness of the lava flow-field in the end of the given period. +Volume erupted since 2022-08-03 before activity started. ++Time-averaged discharge rate for the given period #Extrapolated value. Survey does not cover entire active lava area. ##End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset. Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>. Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a> Data naming conventions: <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> Data Specifications: <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>

Cross-education of Skill: Assessment of Overhand Throwing Using Product- and Process-oriented Assessment.

Data used for Cross-education of Skill: Assessment of Overhand Throwing Using Product- and Process-oriented Assessment.

Primary collected data for modelling the additive MAR/R process by means of the PBF-LB process based on the example of tool steel 1.2709 powder

For the evaluation of a MAR/R process, not only process-, material- and demonstrator-specific correlations and data must be combined. In addition to secondary data (e.g. databases, publications, etc.), primary data (e.g. process times, volume flows, etc.) must also be collected for the specific application. The attached table shows the primary data to be collected for the cradle-to-gate process depending on the process phases and steps. This data is used as support for ecological as well as economic process and component evaluations.

Processed GPR data of Mosvatnet, Norway

SEG-Y files corresponding to the processed GPR data collected on- and offshore Mosvatnet, Stavanger, Norway. In the GPR radargram is possible to identify and map Quaternary glacial and post-glacial sediments and non-natural infill materials.

MMCFSv2 and MMCFSv1 Processed data for GMD Manuscript

This dataset was used to make all the plots in the manuscript titled "Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations" submitted to GMD

opencc-zeroMay 2023View details →

dryad36/100

Habitat isolation interacts with top-down and bottom-up processes in a seagrass ecosystem

Habitat loss is accelerating at unprecedented rates, leading to the emergence of smaller, more isolated habitat remnants. Habitat isolation adversely affects many ecological processes independently, but little is known about how habitat isolation may interact with ecosystem processes such as top-down (consumer-driven) and bottom-up (resource-driven) effects. To investigate the interactive influence of habitat isolation, resource availability and consumer distribution and impact on community structure, we tested two hypotheses using invertebrate and algal epibionts on temperate seagrasses, an ecosystem of ecological and conservation importance. First, we hypothesized that habitat isolation will change the structure of the seagrass epibiont community, and isolated patches of seagrass will have lower epibiont biomass and different epibiont community composition. Second, we hypothesized that habitat isolation would mediate top-down (i.e., herbivory) and bottom-up (i.e., nutrient enrichment) control for algal epibionts. We used observational studies in natural seagrass patches, and experimental artificial seagrass to examine three levels of habitat isolation. We further manipulated top-down and bottom-up processes in artificial seagrass through consumer reductions and nutrient additions, respectively. We indeed found that habitat isolation of seagrass patches decreased epibiont biomass and modified epibiont community composition. This pattern was largely due to dispersal limitation of invertebrate epibionts that resulted in a decline in their abundance and richness in isolated patches. Further, habitat isolation reduced consumer abundances, weakening top-down control of algal epibionts in isolated seagrass patches. Nutrient additions, however, reversed this pattern, and allowed a top-down effect on algal richness to emerge in isolated habitats, demonstrating a complex interaction between patch isolation and top-down and bottom-up processes. Habitat isolation may therefore shape the relative importance of central processes in ecosystems, leading to changes in community composition and food web structure in marine habitats.

TFG Systematization process of generating semantic data and ontology

<pre>Set of TALIS files, the main source of data for the investigation, in CSV format.General ontology manually and by new software. Semantic data, as well as the DSL code. Finally, the web design of the new tool.</pre>

Illustration of the academic publication process

Illustration of the academic publication process, created for Open Educational Ressources provided for the Center for Reproducible Science at the University of Zurich.