cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7661
title: Us Department Of Energy Funded Publications From Astrophysics Data System

description: The John G. Wolbach Library, in collaboration with the NASA Astrophysics Data System (ADS), has compiled this bibliography of United States Department of Energy (DOE) funded publications. Grants were identified within the ADS by regular expression matching against article full text. There are over 70,000 articles in the bibliography, with bibliographic information for each publication. This bibliography is being released to explore its possible usage.

Please note:

Papers may include arXiv preprints (as non-refereed, with arXiv bibcode) and their subsequent journal articles (as refereed, with journal bibcode) as separate and unlinked records

This compilation is a snapshot in time for DOE grants and ADS papers

The method of data collection does not differentiate between grants credited with supporting projects and those supporting individuals contributing to projects.

Results are based on available metadata which may be incomplete. This may include incomplete records of grant identifiers, due to limitations within the ADS. However, all articles in the bibliography acknowledge the support of the DOE in some way.

Results have not been individually verified

All abstracts and articles in the ADS are copyrighted by the publisher, and their use is free for personal use only. For more information, please read the Terms and Conditions regulating usage of resources.

This dataset contains bibliographic information only, no information on the grants themselves is included. A potential application of the data could be to link the bibliographic information to grant information.

<br>
Links

Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library http://www.cfa.harvard.edu/lib/information/about.html

NASA ADS http://labs.adsabs.harvard.edu/

USA Spending: information on US government spending, including the Department of Energy: http://www.usaspending.gov

Grants.gov, more information on US government grants: http://www.grants.gov

Department of Energy: http://energy.gov/
DOI: 10.15468/dl.jvs9gf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
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 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048066-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.3247649
title: Global Soil Moisture Agricultural Drought Index (SMADI)

description: <br>
This repository contains the global Soil Moisture Agricultural Drought Index (SMADI) spanning from June 2010 to December 2015 at fortnightly (bi-weekly) rate. The product is gridded in a 0.05deg Climate Modeling Grid.<br>
<br>
The data are provided in Matlab format (.mat). Each file is a 3600x7200 matrix size, where 3600 is the number of pixels in Latitude coordinates and 7200 to Longitude coordinates, both in the WGS84 system. The center-pixel coordinates are variables "Latitude" and "Longitude". No data values (NaN) correspond to pixels where the retrieval was not possible.<br>
<br>
<br>
SMADI is computed using satellite time series of MODIS Normalized Difference Vegetation Index (NDVI, computed from daily reflectances MOD09CMG v.6), MODIS Land Surface Temperature (LST, product MOD11C1 v.6), and SMOS BEC L3 soil moisture data v2.0 (which corresponds to SMOS L2 v.620, average of ascending and descending orbits). <br>
 <br>
The IGBP land cover (MODIS MOD12C1 product) is used prior to SMADI calculation to select only grassland and cropland/natural vegetation mosaic pixels as representative of the primary agro-ecosystems.
description: This study was supported by:
-The Spanish Ministry of Economy and Competitiveness, MINECO (Project AYA2012-39356-C05). 
-The Spanish Ministry of Science, Innovation and Universities (Projects ESP2015-67549-C3-3 and ESP2017-89463-C3-3-R)
-The Castilla y León Regional Government (Project SA007U16).
-The European Regional Development Fund (ERDF).
description: {"references": ["S\u00e1nchez, N.; Gonz\u00e1lez-Zamora, \u00c1.; Piles, M.; Mart\u00ednez-Fern\u00e1ndez, J. A New Soil Moisture Agricultural Drought Index (SMADI) Integrating MODIS and SMOS Products: A Case of Study over the Iberian Peninsula. Remote Sens. 8, 287, 2016.", "S\u00e1nchez, N.; Gonz\u00e1lez-Zamora,  \u00c1.; Mart\u00ednez-Fern\u00e1ndez, J.; Piles, M.; Pablos, M. Integrated remote sensing approach to global agricultural drought monitoring, Agricultural and Forest Meteorology, Vol 259, pp. 141-153, 2018.", "Pablos, M., Gonz\u00e1lez-Zamora, \u00c1., S\u00e1nchez, N. and Mart\u00ednez-Fern\u00e1ndez, J. (2018). Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture. Proceedings of the International Association of Hydrological Sciences (Proc. IAHS), 95, 1-12."]}
DOI: 10.1594/pangaea.896101
title: Lowered ADCP (LADCP) data during POLARSTERN cruise PS103

description: The lowered ADCP system (LADCP) consist of two RDI Workhorse Monitor ADCP 300 kHz mounted to the CTD-Rosette frame.Near the sea surface, shipboard ADCP (SADCP) data were used to constrain the velocities. The data was processe with GEOMAR LADCP software Version 10.21: 2015-05-17More details can be found in https://doi.org/10.2312/BzPM_0710_2017
DOI: 10.5281/zenodo.820588
title: Ionic Influences On Recombination In Perovskite Solar Cells

description: Data for our paper: "Ionic Influences on Recombination in Perovskite Solar Cells"  <strong>DOI: </strong>10.1021/acsenergylett.7b00490
DOI: 10.1594/pangaea.438922
title: Micromorphological observations in the Miocene section of sediment core CRP-1 (Table 1)

description: Five samples of diamicts from different levels of the Miocene part of the CRP-1 core have been thin sectioned. Observations in the thin sections relate to texture, diagenesis, structure and plasmic fabric. The combination of observed microstructures leads to the interpretation that three samples are certainly indicative of grounded ice, e.g. they are basal tills, whilst the other two samples also possibly represent the same environment.
description: Supplement to: van der Meer, Jaap; Hiemstra, John F (1998): Micromorphology of Miocene diamicts, indications of grounded ice. Terra Antartica, 5(3), 363-366
DOI: 10.5281/zenodo.3726551
title: Dataset for Metabolic Cost Calculations of Gait using Musculoskeletal Energy Models, a Comparison Study

description: This data set contains raw and processed data of gait analysis experiments of level and inclined walking at two speeds for 12 participants. The slopes were uphill and downhill with 8% incline. The raw data contains the output of the force plates and marker data, as well as raw measurements from an K4B2 system. Mat files are processed data: measured metabolic rate, and measured and calculated metabolic cost, as well as kinetic and kinematic data of an averaged gait cycle: joint angles, velocities and moments, ground reaction forces, muscle activation, contractile element length and stimulation, and the duration of the gait cycle.
DOI: 10.5281/zenodo.4569556
title: Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)

description: We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em> The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. <strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[url]</strong> <strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[url]</strong> Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to 10.5281/zenodo.8084388. If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn. If you want to use <strong>monthly</strong> estimates from 2000 to 2022, please go to 10.5281/zenodo.8084388. <strong>We also estimate other atmospheric data:</strong> For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.
DOI: 10.5281/zenodo.4267996
title: ESC Dataset

description: ESC Dataset presented in the paper <strong>Elastic Smart Contracts in Blockchains</strong> (10.1109/JAS.2021.1004222) IEEE/CAA Journal of Automatica Sinica ( Volume: 8, Issue: 12, December 2021 In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.
DOI: 10.5281/zenodo.4061502
title: LMD Inconel 718 Spiral 2020-07-31 left

description: Description of dataset 10.5281/zenodo.4061502 Deposition of Inconel 718 square spiral with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocity = 600 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Spiral size: 60 mm side, 3 mm step<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient This experiment took place on the same substrate of 10.5281/zenodo.4049842 (on the "left") and follows the same part program. In this case, the laser spot is slightly off-center in camera images. The dataset is constituted by:<br> - Melt pool images, in file Deposition_2020_07_31__13_09_50.zip, acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trackData_20200731_left.csv containing:<br> - t: timestamp in ms. Synchronized with imData_20200731_left.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imData_20200731_left.csv containing:<br> - t: timestamp in ms. Synchronized with trackData_20191205_left.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)
DOI: 10.5281/zenodo.4278953
title: GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020

description: The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset.
DOI: 10.5281/zenodo.6407548
title: 2019-2020 AR station alpine meadow ecosystem tower-based observation spectra, GPP and meteorological data

description: This is the dataset used in the <em>Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems</em> paper. The dataset contains canopy red and far-red SIF data, GPP data, NDVI data, photosynthetically active radiation(PAR) data, temperature(Ta) data, and vapor pressure deficit(VPD) data during the 2019 and 2020 growing seasons in the alpine meadow ecosystem at the AR site(100.4643 E, 38.0473 N, altitude 3033 m).
DOI: 10.5281/zenodo.6533849
title: IODP Expedition 374 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.4603483
title: Manifestations of magnetotail flow channels in energetic particle low-altitude observations

description: The names of RCM-I simulation output files specify the simulation time in a format of "hhmmss". The stored quantities are, "I","J","COLAT","ALOCT","MLT","BNDLOC","XMIN","YMIN","FTV","BMIN","V", "BIRK_fromV(NH)","P(RCM),nPa", "PV_gamma","Vtotx", "Vtoty","Vx_exb","Vy_exb","vel_x","vel_y","Ey","PEDLAM", "PEDPSI", "HALL", "RCM_T_p","RCM_T_e","RCM_N_e","EFLUX","EAVG","f_i_50-75","f_i_75-113","f_i_113-170","f_i_170-250","f_i_39-115","f_e_50-75","f_e_75-105","f_e_105-150","f_e_150-225","f_e_225-315","f_e_40-20000","Vm".<br> For the first one hour of substorm growth phase, we upload output files with a ten minutes time interval . For 60-75min, 20 seconds and for 75-90min, one minute.
DOI: 10.1594/pangaea.706306
title: (Table 1) Rock type and summary of alteration after component minerals in thin sections of ODP Hole 176-735B gabbros
DOI: 10.5281/zenodo.1476503
title: A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Masked high dynamic range data

description: Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement): <strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file. <strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning. <strong>@.mhdr.h5</strong>: HDF5 file containing masked HDR raw scan data. Masked values are represented by nan. The zip files correspond to the sample IDs, according to the following table: <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre>
DOI: 10.15468/dl.erxrex
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.019777 -35.870434, 147.019777 -35.877171, 147.024268 -35.877171, 147.024268 -35.881663, 147.026514 -35.881663, 147.026514 -35.879417, 147.031006 -35.879417, 147.031006 -35.877171, 147.033251 -35.877171, 147.033251 -35.872679, 147.024268 -35.872679, 147.024268 -35.870434, 147.019777 -35.870434))"
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}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048067-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.4335594
title: MAVIS Twitter dataset: A collection of tweets and sentiment analysis in Spanish about vaccines and diseases during the period 2015-2018

description: MAVIS dataset comprises a full knowledge base regarding Twitter messages published in Spanish during the period 2015-2018, in the context of sentiment analysis of specific vaccines and their related diseases. Such diseases and vaccines are summarized as follows: Invasive meningococcal disease (“EMI” in Spanish): Bexsero, Trumenba, Nimenrix Invasive pneumococcal disease (“ENI” in Spanish) Influenza Hepatitis Rotavirus: Rotarix, Rotateq Measles (“Sarampión” in Spanish) and MMR (“Triple vírica” in Spanish) Sepsis Whooping cough (“Tosferina” in Spanish) Chickenpox (“Varicela” in Spanish): Varivax, Varilrix; and Shingles (“Zoster” in Spanish) Human papillomavirus infection (“VPH” in Spanish): Cervarix, Gardasil Tweets have been manually classified as having a negative or non-negative sentiment by 5 experts. Moreover, an automatic classification has been performed by 3 different tools: IBM Watson (now Watson Tone Analyzer, https://www.ibm.com/watson/services/tone-analyzer/), Google Cloud Natural Language (https://cloud.google.com/natural-language), and Meaning Cloud (https://www.meaningcloud.com/). IBM Watson and Google Cloud Natural Language returned a numerical sentiment score ranging from -1 to 1, while Meaning Cloud returned a categorical variable with the values ‘P+’, ‘P’, ‘NEU’, ‘N’ and ‘N+’, which were converted to 1, 2, 3, 4 and 5 respectively. With these variables (IBM Watson, Google Cloud Natural Language, and Meaning Cloud annotations and the experts’ classification as the target label), a machine learning metamodel was developed. Tweets were also annotated with the sentiment output given by this classifier. The provided data includes intrinsic tweets information, intrinsic information regarding the users that posted the tweets, the keywords mentioned in each tweet, and the annotations that the experts, the tools, and the model gave to each tweet. <strong>Funding</strong>: This dataset was obtained with funding from MSD, Spain under MAVIS Study (VEAP ID: 7789). <strong>Current studies using this dataset at the moment of the publication</strong>: Rodríguez-González et al., “Creating a metamodel based on machine learning to identify the sentiment of vaccine and disease-related messages in Twitter: the MAVIS study” in 2020 IEEE 33st International Symposium on Computer-Based Medical Systems (CBMS), Jul. 2020, p. 6. DOI: 10.1109/CBMS49503.2020.00053 Rodríguez-González et al., "Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques" in Applied Sciences, 2020, 10. DOI: 10.3390/app10249019
DOI: 10.5281/zenodo.10394325
title: Replication Dataset of the paper "Towards Better Understanding of Breaking Changes in NPM Ecosystem"

description: Replication Dataset of the paper "Towards Better Understanding of Breaking Changes in NPM Ecosystem"
DOI: 10.5281/zenodo.10394326
title: Replication Dataset of the paper "Towards Better Understanding of Breaking Changes in NPM Ecosystem"

description: Replication Dataset of the paper "Towards Better Understanding of Breaking Changes in NPM Ecosystem"