cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2mn4n9j77k.1
title: Data for: Unveiling differential thermal sensitivities in marine phytoplankton

description: The following supplementary dataset consists of information of surface (&lt; 5 m) phytoplankton community composition and paired temperature collected in China Seas from 2002 to 2015.
DOI: 10.17909/9293-cs53
title: JWST SN 2022pul

description: JWST data of SN 2022pul presented in Siebert et al. 2023b, Kwok et al. 2023b, and Johansson et al. 2023.
DOI: 10.21335/nmdc-226850212
title: Chlorophyll A and phaeopigments Nansen Legacy cruise 2019711

description: This dataset is a collection of the acid-corrected chlorophyll A and phaeopigments measurements taken as part of the Nansen Legacy project (www.arvenetternansen.com), as part of the '2019 Seasonal Study Q4' cruise.
DOI: 10.21335/nmdc-966499899
title: Chlorophyll A and phaeopigments Nansen Legacy cruise 2021704

description: This dataset is a collection of the acid-corrected chlorophyll A and phaeopigments measurements taken as part of the Nansen Legacy project (www.arvenetternansen.com), as part of the '2021 Seasonal Study Q2' cruise.
DOI: 10.24381/cds.7e59b01a
title: Leaf area index and fraction absorbed of photosynthetically active radiation 10-daily gridded data from 1981 to present
DOI: 10.25318/1410029701-eng
title: Labour force characteristics by occupation, annual, inactive

description: Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by National Occupational Classification (NOC) and sex, last 5 years.
DOI: 10.25386/genetics.7370168
title: Supplemental Material for Schmidt et al., 2019

description: <b>Data used in Example 3 and Example 4. </b><br>The <b>phenotypic data </b>hast columns "loc", "gen", "rep", and "y", which denote the environment, gentoype, replicate (i.e. block) and response variable. The<b> marker data</b> contains the kinship matrix K with dimensions 89x89 - for the 89 genotypes in the phenotypic data.<br><br>Results based on this dataset were first published in Hadasch <i>et al. </i>(2016). See our article for more detailed information.
DOI: 10.25430/researchdata.cab.unipd.it.00001279
title: Martian simulant analysis dataset

description: This dataset contains data derived from chemical, mineralogical, granulometric and hyperspectral acquisitions of Mars Global (MGS-1) High-Fidelity Martian Dirt Simulant [1], Mojave Mars Simulant MMS-1 and Enhanced Mars Simulant (MMS-2). The instruments used for this work are:
- Laser Diffraction Particle Size Analyzer Malvern Panalytical Mastersizer3000: granulometric analysis;
- Inductively Coupled Plasma Mass Spectrometer (ICP-MS) Perkin-Elmer NexION 350X: chemical analysis;
- X-Ray powder Diffractometer (XRD) Philips X’Pert PRO: mineralogical analysis;
- Scanning Electron Microscope (SEM-EDS) Tescan SOLARIS equipped with Oxford Instruments microanalytical system: mineralogical analysis.
- Headwall Photonics Nano-Hyperspec (400-1000 nm) and Micro-Hyperspec (900-2500 nm) cameras: hyperspectral acquisitions;
The Mastersizer3000 software creates tables and related plots of grainsize ready to use.
The detector of mass spectrometer generates data in CPS (counts per second). Knowing the dilutions of the solutions, the spectrometer software calculates the quantities in terms of weight over volume (µg/l) in a table. Thereafter, knowing the initial weights of the simulant in the solutions, the operator converts data from weight over volume (µg/l) to weight over weight (mg/kg). 
Qualitative phase analysis on powder diffraction data has been run through a search-match algorithm, in order to identify the minerals species. Quantitative phase analysis has been performed using the Rietveld method with internal standard addition, as implemented in Profex-BGMS v. 5.2.3. 
The SEM products are photos of the sites, where the mineralogical analysis is performed, and graphs with peaks. The peaks are associated with specific chemical elements through the SEM software, that allows to determine the minerals present in the simulant. It was carried out only on the largest grains of MGS-1 simulant.
The hyperspectral cameras acquire hyperspectral cubes that the operator can open in ENVI software [3] or similar ones for hyperspectral imaging data-sets to extrapolate the spectrum of the simulants. The spectrum is saved in table format (ASCII) and can be opened in Origin software [4] or any software dedicated to graph and table management (e.g., Excel). In Origin, the operator produces the spectral plot, where he/she can continue with direct interpretations of absorption peaks, characteristic of particular minerals.
DOI: 10.3886/e198929v1
title: Data and Code for: Why Labor Supply Matters for Macroeconomics

description: This paper presents data on cross country differences in aggregate hours of work and argues that labor supply plays an important role in understanding these differences. <br>
DOI: 10.5067/aura/omi/data1404
title: OMI/Aura Level 1B VIS Global Geolocated Earth Shine Radiances 1-orbit L2 Swath 13x24 km V4
DOI: 10.5281/zenodo.10056054
title: PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - May 2021)

description: PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)
Build Type: Class-Standard Relations-OWLNETS
Build Date: May 01, 2021
 
Important Build Information
The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (here). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.
Details on each of the files generated by the build process can be found in the file associated with this directory (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx).
 
🚨 AVAILABLE FILES 🚨 

Available KG benchmark files are zipped and listed below.
For additional details on what each file contains, please see the associated Wiki page 👉 here.
DOI: 10.5281/zenodo.10056056
title: PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - May 2021)

description: PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)
Build Type: Class-Inverse Relations-OWLNETS
Build Date: May 01, 2021
 
Important Build Information
The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (here). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.
Details on each of the files generated by the build process can be found in the file associated with this directory (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx).
 
🚨 AVAILABLE FILES 🚨 

Available KG benchmark files are zipped and listed below.
For additional details on what each file contains, please see the associated Wiki page 👉 here.
DOI: 10.5281/zenodo.11553365
title: A Novel Surface Energy Balance Method for Thermal Inertia Studies of Terrestrial Analogs

description: Thermophysical data collected from Woodhouse Mesa, AZ, USA in May 2021 and Sept 2022
DOI: 10.5281/zenodo.12216500
title: Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements Dataset

description: Dataset containing simulation results and example input scripts for the paper "Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements".
DOI: 10.5281/zenodo.13133835
title: Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"

description: Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" 

tmdcm.txt: current meter data 

tide.txt: tidal gauge data 

windspeed.txt: windspeed data 

Figure 2: Figure2.npy

Figure 3: Figure 3 abc .npy

Figure16:  spatial_Vc.npy & spatial_h.npy 

Figure 17: streching_ncf.npy
DOI: 10.5281/zenodo.13685344
title: Supplements for dynamically triggered events in the Caucasian Region

description: the repository contains seismic data and related products (Table S1 and S2) used in studying dynamically triggered events in Caucasian Region.
DOI: 10.5281/zenodo.13820408
title: Pyrfume: A window to the world's olfactory data

description: Advances in theoretical understanding are often fueled by access to large, diverse experimental datasets. Olfactory neuroscience has lagged behind other sensory fields, largely due to the scarcity of comprehensive datasets that connect olfactory stimuli with corresponding percepts, behaviors, and neural circuits. To address this, we developed a unified framework, called Pyrfume, designed to integrate and standardize dozens of olfactory datasets, spanning different species and sensory modalities. This effort aims to provide a much-needed resource for researchers, accelerating progress in unraveling the complexities of olfaction and bringing the field closer to the level of understanding achieved in other sensory systems. The Pyrfume repository is notable for its breadth and coverage, spanning >40 odorant-linked datasets in mammalian olfaction including human psychophysics and perception; and animal psychophysics, behavior, brain imaging, physiology, and pharmacology. The foundation of this archive is built on two straightforward principles: 1) most olfactory experiments can be easily indexed by the unique identifier (ID) of an olfactory stimulus, whether it’s a molecule, substance, or mixture at a specified concentration; and 2) any olfactory experiment can be described in a universally readable format as a combination of chemical identifiers, the task performed with the stimuli, and the observed individuals along with their corresponding behaviors. By adhering to these principles, Pyrfume enables scientists, engineers, and trainees to delve into, evaluate, and explore the vast landscape of olfactory research and facilitates further cross-experiment analysis and discovery. This repository is also available to access via https://github.com/pyrfume.
DOI: 10.5281/zenodo.13831107
title: Data Supporting The Paper 'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection'

description: The dataset includes raw images, source code, raw vectors saved from MATLAB, and curated data used to generate figures and analyses in the paper 'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection' by Z. Yu, et al.

Additional information about the experiments will be available upon request.
DOI: 10.5281/zenodo.13838208
title: RoboCup 2023-2024 ROSbag Dataset

description: Robocup 2023

Repository name: Robocup at Home 2023 Rosbags Dataset

Data identification number: 10.5281/zenodo.13902513

Direct URL to data: https://zenodo.org/records/13902513

Robocup 2024

Repository name: Robocup@Home 2024: Recepcionist Rosbags Dataset

Data identification number: 10.5281/zenodo.13902011

Direct URL to data: https://zenodo.org/records/13902011

 

Repository name: Robocup at Home 2024 Rosbags Dataset for Carry my luggage

Data identification number: 10.5281/zenodo.13748065

Direct URL to data: https://zenodo.org/records/13748065

 

Repository name: Robocup at Home 2024 Rosbags Dataset for GPSR

Data identification number: 10.5281/zenodo.13902406

Direct URL to data: https://zenodo.org/records/13902406

Repository name: Robocup at Home 2024 Rosbags Dataset for storing groceries

Data identification number: 10.5281/zenodo.13749419

Direct URL to data: https://zenodo.org/records/13749419

 

Repository name: Robocup at Home 2024 Rosbags Dataset for stickler for the rules

Data identification number: 10.5281/zenodo.13902378

Direct URL to data:  https://zenodo.org/records/13902378

Repository name: Robocup at Home 2024 Rosbags Dataset for restaurant

Data identification number: 10.5281/zenodo.13902310

Direct URL to data: https://zenodo.org/records/13902310
DOI: 10.5281/zenodo.13911734
title: Low lAtitude long Range Ionospheric raDar (LARID) and related ionospheric data

description: The low latitude long range ionospheric radar (LARID) echo, Global Navigation Satellite System (GNSS) data, ionosonde data, and all-sky VHF radar echo data are from the Ionospheric observational network for irregularity and scintillation in East/Southeast Asia (IONISE), the Chinese Meridian Project (http://data.meridianproject.ac.cn), and are archived at the Geophysics Center, National Earth System Science Data Center at BNOSE, IGGCAS (http://wdc.geophys.ac.cn/). The LARID at Dongfang are used to investigate the ionospheric irregularities and disturbances in a wide longitude span ~90 degree or more. The LARID worked at 20.4 MHz under a beam steering mode within the azimuth -24°-24° (clockwise due boresight of the radar in the east and west directions), stepped by 3°. For each beam, the radar can receive signals from the slant ranges of 45 km to 4500 km or more, with typical temporal (range) resolutions ranging between 1 and 8 min (15 and 60 km). In addition, the ionosonde ionograms obtained at Chiang Mai, and Ledong, with temporal resolutions of 7.5 min were manually scaled to get foEs and the virtual height of different plasma frequency. The echoes from the all-sky VHF radar at Ledong are used to investigate E region irregularities. The TEC and S4 index with resolutions of 30 s and 1 min respectively are used to investigate different kinds of ionospheric irregularities and disturbances.
DOI: 10.5281/zenodo.4085098
title: Leffingwell Odor Dataset

description: <strong>NOTE: It's easier to download this dataset from pyrfume. Here's how:</strong> <pre><code># First install pyrfume in your Python environment. This can be done easily with pip. # pip install pyrfume import pyrfume molecules = pyrfume.load_data('leffingwell/molecules.csv', remote=True) behavior = pyrfume.load_data('leffingwell/behavior.csv', remote=True) # e.g. to count the number of molecules with each descriptor behavior.sum().sort_values(ascending=False).astype(int) </code></pre> Predicting properties of molecules is an area of growing research in machine learning, particularly as models for learning from graph-valued inputs improve in sophistication and robustness. A molecular property prediction problem that has received comparatively little attention during this surge in research activity is building Structure-Odor Relationships (SOR) models (as opposed to Quantitative Structure-Activity Relationships, a term from medicinal chemistry). This is a 70+ year-old problem straddling chemistry, physics, neuroscience, and machine learning. To spur development on the SOR problem, we curated and cleaned a dataset of 3523 molecules associated with expert-labeled odor descriptors from the <em>Leffingwell PMP 2001</em> database. We provide featurizations of all molecules in the dataset using bit-based and count-based fingerprints, Mordred molecular descriptors, and the embeddings from our trained GNN model (Sanchez-Lengeling et al., 2019). This dataset is comprised of two files: <strong>leffingwell_data.csv</strong>: this contains molecular structures, and what they smell like, along with train, test, and cross-validation splits. More detail on the file structure is found in leffingwell_readme.pdf. <strong>leffingwell_embeddings.npz</strong>: this contains several featurizations of the molecules in the dataset. <strong>leffingwell_readme.pdf</strong>: a more detailed description of the data and its provenance, including expected performance metrics. <strong>LICENSE</strong>: a copy of the CC-BY-NC license language. The dataset, and all associated features, is freely available for research use under the CC-BY-NC license. If you use the data in a publication, please cite: <pre>@article{sanchez2019machine, title={Machine learning for scent: Learning generalizable perceptual representations of small molecules}, author={Sanchez-Lengeling, Benjamin and Wei, Jennifer N and Lee, Brian K and Gerkin, Richard C and Aspuru-Guzik, Al{\'a}n and Wiltschko, Alexander B}, journal={arXiv preprint arXiv:1910.10685}, year={2019} }</pre>
description: {"references": ["Sanchez-Lengeling et al. (2019). Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules. arXiv:1910.10685"]}
DOI: 10.5281/zenodo.4587954
title: Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2

description: These training fasta files are derived from the Silva Project's version 138.1 release and formatted for use with DADA2. These files are intended for use in classifying prokaryotic 16S sequencing data and are not appropriate for classifying eukaryotic ASVs. See https://benjjneb.github.io/dada2/training.html for information about DADA2 reference databases and https://www.arb-silva.de/documentation/release-138.1/ for database and citation information for Silva 138.1. The Silva 138.1 database is licensed under Creative Commons Attribution 4.0 (CC-BY 4.0); see file "SILVA_LICENSE.txt". These fasta database files were generated and checked for consistency using the R markdown documents in the silva-138.1 folder in https://zenodo.org/record/4587946. If you use these files, please cite one or both of the Silva references below (or at the above link) and the DADA2 paper (reference below). I also recommend citing or linking to the Zenodo record for this specific version in your Methods or published source code to record the specific taxonomic database files used in your analysis. <strong>NOTE:</strong> These database files have a known problem in 3/895 families and 59/3936 genera. See https://github.com/mikemc/dada2-reference-databases/blob/main/silva-138.1/v1/bad-taxa.csv for a list of affected taxa and https://github.com/benjjneb/dada2/issues/1293 for more information.
description: {"references": ["Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Gl\u00f6ckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41 (D1): D590-D596.", "Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, Schweer T, Peplies J, Ludwig W, Gl\u00f6ckner FO (2014) The SILVA and \"All-species Living Tree Project (LTP)\" taxonomic frameworks. Nucl. Acids Res. 42:D643-D648", "Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581\u2013583. doi:10.1038/nmeth.3869"]}
DOI: 10.5281/zenodo.6515019
title: Astra Memory Error and System Monitoring Data Sets

description: Memory error events and system monitoring information gathered on Astra while it was operating from January 20, 2019 to September 14, 2019. See the dataset description files in the archive for additional details.
DOI: 10.5281/zenodo.7625259
title: Radii of S-phase Al2CuMg in Al-alloy EN AW-2618A after different aging times at 190°C

description: The dataset contains data from quantitative microstructural analysis of transmission electron microscopy (TEM) studies of the S-phase (Al<sub>2</sub>CuMg) radii in Al-alloy EN AW 2618A. The investigated material and the applied methods were described in detail in two publications. Further information is provided in the file content.pdf.
DOI: 10.5281/zenodo.7644331
title: A Missing Piece of the E-Region Puzzle: The Need for High-Resolution Photoionization Cross Sections and Solar Irradiance in Models

description: Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: The Need for High-Resolution Photoionization Cross Sections and Solar Irradiance in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure. <strong>***Please note that the paper is under review and has not been accepted yet.***</strong>
DOI: 10.5281/zenodo.8057228
title: WACCPD-MURaM-RawData

description: Here are the raw data for the plots used in the WACCPD 2023 paper submission titled "Analysis of MURaM, a Solar Physics Application for Scalability, Performance and Portability" Here is the GitHub link to where the code resides https://github.com/NCAR/MURaM_main
DOI: 10.5281/zenodo.8099850
title: Monthly outputs of Today's Earth-Global

description: This database includes Today's Earth-Global output, which includes monthly river discharge, snow depth, and soil moisture. Please find the details from Today's Earth.
DOI: 10.5439/1323894
title: aosccn2colaavg.b1

description: CCN200 data, averaged
DOI: 10.6084/m9.figshare.23868531.v1
title: Negative Correlation between Soil Salinity and Soil Organic Carbon Variability

description: Soil Organic Carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability of in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC &lt; 150 g kg-1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m-1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg-1 above the mean predicted SOC ( = 18.47 g kg-1) to 0.46 g kg-1 below (~-430%), while for non-croplands, such decline is sharper, from 0.96 above = 35.96 g kg-1 to 4.99 below (~-620%). Although salinity’s significance in explaining SOC variability is minor (&lt; 6%), we estimate a one std increase in salinity of topsoil samples (0 - 7 cm) correlates with respective declines of ~4.4% and ~9.26%, relative to and . The decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needleleaved trees are estimated with the highest decline in non-croplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index the most significant parameters in explaining the SOC’s variability. The findings provide insights into SOC dynamics under increased soil salinity, enhancing our understanding of SOC stock responses to land degradation and climate warming.The data provided in this dataset forms the foundation for the aforementioned results and helps in replicating the output.
DOI: 10.6084/m9.figshare.26124061
title: Genome Project of<i> Stichopus monotuberculatus</i>

description: Genome Project of<i> Stichopus monotuberculatus</i>, It contains genome assembly ,genome annotation, gene function ,and gene family data.
DOI: 10.6084/m9.figshare.9973469
title: Annotation results of Prunus salicina genome

description: We provide the annotation results of repeated sequences, gene structure, and functional prediction for the de novo assembled genome of Prunus salicina.
DOI: 10.14278/rodare.2817
title: Data publication: Ab initio path integral Monte Carlo simulations of warm dense two-component systems without fixed nodes: structural properties

description: This repository contains the raw data for the publication "Ab initio path integral Monte Carlo simulations of warm dense two-component systems without fixed nodes: structural properties". All units as described in the paper / figure captions.