cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14796083
title: AVIRIS-NG/GAO CO2 emission rates for plumes observed at U.S. power plants in 2022-2023

description: Data to accompany Jinsol et al. (2025):

(1) L4 AVIRIS-NG/GAO CO2 emission rates generated using linearized and lognormal matched filter for plumes observed between 2022-2023 at U.S. power plants with in-stack continuous emissions monitoring systems (CEMS): CEMS_all_plumes_with_emission_linear.csv / CEMS_all_plumes_with_emission_lognormal.csv

(2) L2C AVIRIS-NG/GAO CO2 quality control data: CEMS_all_plumes_QC.csv

(3) CEMS emission rate reported by the U.S. Environmental Protection Agency (EPA), stack height from U.S. EPA's 2022 National Emission Inventory, geographic location, estimated emission rate, emission rate uncertainty, wind speed at 10m, wind speed at stack height for all observations at facility scale as well as solar zenith angle, water vapor, ground elevation, sensor height used for retrieval: CEMS_by_facility. csv

(4) Detection, emission rate, wind speed, pixel resolution, noise parameter used to generate POD model: CEMS_by_unit_for_POD.csv
DOI: 10.5281/zenodo.2635686
title: Hydrolysable carbohydrate data collected from the Trace Metal Rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> Hydrolysable carbohydrate (referred to as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon that is excreted by most (micro)organisms or released by continental margins/sediments. It is a carbon source for heterotrophic bacteria. These carbohydrates could also potentially bind iron and act as an iron binding ligand. This data is used to explore the nature of iron ligands and relate to biological and chemical oceanography. <strong>Dataset contents</strong> ace_hydrolysable_carbohydrates_tpzt_data.csv, data file, comma-separated values ace_tpzt_data_visual_summary.png, metadata, portable network graphics README.md, metadata, text format data_file_header.txt, metadata, text format All missing values where no data point exists from lack of sample, have been set to NaN.
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. This work was also supported by Swiss National Science Foundation grants PP00P2_138955 and PP00P2_166197.
description: {"references": ["Myklestad S, Sk\u00e5n\u00f8y E, Hestmann S, A sensitive and rapid method for analysis of dissolved mono- and polysaccharides in seawater. Mar Chem 56:279\u2013286 (1997)", "Ocean Data View (ver. 4.5). Schlitzer,R.(2012). OceanData View (version4.5)"]}
DOI: 10.5281/zenodo.3250136
title: Humic acid like concentration in seawater samples, collected from the trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> Humic acid like concentration (abbreviated HA) measured with respect to the Suwannee River Fulvic acid standards (µmol SRFA equivalent per litre). Seawater samples were collected from trace metal rosette (TMR) deployments at different depths in the water column during the Antarctic Circumnavigation Expedition (ACE). Humic acid like data from legs 1 and 2, from TMR cast numbers 3 to 16, were analysed by electrochemistry following standard additions of Suwannee River Fulvic Acid (standard 1, IHSS). This data is to support iron ligands and iron bioavailability as well as hydrolysable saccharides (TPZT) data, also collected during ACE. <strong>Dataset contents</strong> ace_humics_data.csv, data file, comma-separated values ace_humics_data_visual_summary.png, metadata, portable network graphics data_file_header.txt, metadata, text format README.txt, metadata, text format <strong>Dataset license</strong> This humics dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.
description: {"references": ["https://www.frontiersin.org/articles/10.3389/fmars.2016.00289/full"]}
DOI: 10.5281/zenodo.3634411
title: Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition (2016-2017)

description: Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017. Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (&gt;6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20).
description: These data were collected as part of the Antarctic Circumnavigation Experiment, which was organized by the Swiss Polar Institute with funding from Ferring Pharmaceuticals. This dataset is accompanied by the following open access publication: Janssen, D.J., Sieber, M., Ellwood, M.J., Conway, T.M., Barrett, P.M., Chen, X., de Souza, G.F., Hassler, C.S., Jaccard, S.L. (2020). Trace metal and nutrient dynamics across broad biogeochemical gradients in the Indian and Pacific sectors of the Southern Ocean. Marine Chemistry. https://doi.org/10.1016/j.marchem.2020.103773
description: {"references": ["Janssen, D.J., Sieber, M., Ellwood, M.J., Conway, T.M., Barrett, P.M., Chen, X., de Souza, G.F., Hassler, C.S., Jaccard, S.L. (2020). Trace metal and nutrient dynamics across broad biogeochemical gradients in the Indian and Pacific sectors of the Southern Ocean. Marine Chemistry. https://doi.org/10.1016/j.marchem.2020.103773"]}
DOI: 10.5281/zenodo.5705391
title: IPCC Working Group 1 (WG1) Sixth Assessment Report (AR6) Annex III Extended Data

description: Extended data relating to atmospheric abundences and effective radiative forcing from historical and future projections. Data is presented in abridged form in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) Working Group 1 (WG1) Annex 3. In this dataset, data is provided for all years, and includes additional scenarios not included in the published tables. <strong>Contents</strong> table A3.1: historical observed greenhouse gas (GHG) abundances. All subtables a-f in the printed report are combined into one CSV file. table A3.2: future projections (2020-2500) of GHG abundances for nine SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0-lowNTCF, SSP4-3.4, SSP4-6.0, SSP5-3.4-over, SSP5-8.5). Orignal data is from Meinshausen et al. (2020): https://doi.org/10.5194/gmd-2019-222 table A3.3: historical effective radiative forcing (ERF) for 1750-2019 (unit is W m<sup>-2</sup>) best estimate 5th percentile 95th percentile 100000 member Monte Carlo ensemble (HDF file) table A3.4: future projections of ERF from 1750-2500 (including historical to 2014, projections starting from 2015). Unit is W m<sup>-2</sup>. table A3.4a: SSP1-1.9 (best estimate, 5th and 95th percentile) table A3.4b: SSP1-2.6 (best estimate, 5th and 95th percentile) table A3.4c: SSP2-4.5 (best estimate, 5th and 95th percentile) table A3.4d: SSP3-7.0 (best estimate, 5th and 95th percentile) table A3.4e: SSP5-8.5 (best estimate, 5th and 95th percentile) table A3.4f: breakdown of minor greenhouse gases, and aggregated categories, for the five Tier 1 SSP scenarios in tables A3.4a to A3.4e (best estimate) tables A3.4x: tables A3.4a to A3.4f for Tier 2 SSP scenarios: SSP3-7.0-lowNTCF SSP3-7.0-lowNTCFCH4 SSP4-3.4 SSP4-6.0 SSP5-3.4-over table A3.5: projections of ERF from 1750-2500 from RCP2.6, RCP4.5, RCP6.0 and RCP8.5 using AR6 assessment (best estimate, 5th and 95th percentile, breakdown of minor gases; unit is W m<sup>-2</sup>) <strong>Citation</strong> IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative forcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In <em>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</em> [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press.
DOI: 10.6073/pasta/3ddd2522e3361f858f30c411b1adf815
title: Monthly small nekton samplings collected in the Plum Island Estuary, 2015
DOI: 10.6084/m9.figshare.28279442
title: Supplementary Material for: Markers of Prognosis for Acute Esophageal Necrosis: A Systematic Review

description: Introduction: Acute esophageal necrosis (AEN) is a rare and lethal condition that may progress to sepsis and perforations. Most related literature comes from case reports; however, a few small reviews have been published. We conducted a large systematic review of AEN using PubMed, MEDLINE, and Embase to organize data into one consolidated manuscript, find potential prognosticators of illness, and determine possible treatment guidelines for AEN. 
Methods: Advanced searches were performed of all English case reports from 1990 to 2021 using medical subject heading (MeSH) terms. Data on patient age, sex, comorbidities, initial presentation, management, progression of illness, and hospital survival were collected.
Results: Our study included 226 articles, encompassing 319 cases. 32.3% of patients had diabetes, 26.6% had hypertension, and 19.7% had alcohol use disorder. 66.5% presented with an upper gastrointestinal bleed and 21.9% developed sepsis or esophageal perforation. 60.9% of patients were reported to have survived their illness, but 16.6% of cases did not have their discharge status documented. Interestingly, patients presenting with pain or ketoacidosis demonstrated improved survival.
Conclusion: AEN becomes more prevalent as patients age and develop cardiovascular disease, which increases the risk of developing a hypoperfusive state and mucosal injury to the distal esophagus. Early fluid resuscitation, acid-reducing agents, and bowel rest may serve as potential lifesaving interventions, and antibiotics should be considered if there is concern for infection. Patients require close follow up in anticipation of impending stricture.
DOI: 10.6084/m9.figshare.28463939
title: Naturalized alien plants experience less negative soil-legacy effects and gain competitive advantages through spatial heterogeneity

description: The file includes the data, code and readme file of the paper "Naturalized alien plants experience less negative soil-legacy effects and gain competitive advantages through spatial heterogeneity" by Duo Chen and Mark van Kleunen.
DOI: 10.6084/m9.figshare.28731476
title: Supplementary Material for: Fibrillary Glomerulonephritis and Multiple Myeloma, A Case Report and Literature Review

description: Introduction: Fibrillary glomerulonephritis (FGN) is a rare form of immune complex-mediated primary glomerular disease frequently coexisting with malignancies or autoimmune diseases. The kidney prognosis is extremely poor, with approximately 50% of patients progressing to end-stage kidney disease within two to four years after diagnosis. However, no established treatment currently exists.
Case Presentation: Here we describe a rare case of FGN diagnosed in a patient progressing from monoclonal gammopathy to multiple myeloma. The histopathological findings of the kidney biopsy were consistent with classical FGN and revealed no evidence of myeloma cast nephropathy. Albumin-dominant, Bence Jones protein-negative proteinuria further supported this diagnosis. The patient was successfully treated with anti-myeloma chemotherapies including autologous stem cell transplant, resulting in significant improvement in kidney function.
Conclusion: Based on our experience, secondary FGN associated with plasma cell neoplasms may represent a rare entity that responds favorably to anti-myeloma therapies. Initial investigations to rule out coexistent plasma cell neoplasms are crucial for the optimal management of FGN patients.
DOI: 10.15468/dl.s9rjrp
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhacophorus kio Ohler & Delorme, 2006"
 ]
}

 The dataset includes 24 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006985-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15162181
title: Complete List of Mathematical Expressions in all Wikimedia Projects, including Wikipedia

description: This dataset contains a deduplicated list of all mathematical expressions used in all wikimedia projects. The data is provided as json file where the key is the md5 hash of the input. The input is what was extracted from the wikitext sources. This was done in the following way:



All current dump were filtered for the math tag (see https://doi.org/10.5281/zenodo.15107679) for details

Those dumps were imported into a mediawiki installation with the MathSearch extension. Here one database was used per wiki.

The data from all the mathlog tables were combined in one table, which was exported into a json file. The json contains a list of key value pairs where the keys are the md5 hashes of the input.


The scripts are available from

swh:1:cnt:faec2206a154db5a2711791f4211097e36bf1413; origin=https://github.com/MaRDI4NFDI/wikiFilter; visit=swh:1:snp:28ed43d0e16ca3d6ce4bad1b484cec9d1124cd48; anchor=swh:1:rev:855735a5c90a0db3ccfd20c3899af4c82bc6704f; path=/wmcloud/allFormulae.sqlExample: The Wikipedia article on mass energy equivalence contains the following wikitext



<math qid=Q35875>E = mc^2</math>


the MathSearch extension extracts the user input



E = mc^2


the md5 hash is 



281a70c20b16a38d7781189936e1ac9f


and thus the row



    "281a70c20b16a38d7781189936e1ac9f": "E = mc^2",


in the json file corresponds to that input.
description: Except as discussed below, all original textual content is licensed under the  GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 4.0 License. Some text may be available only under the Creative Commons license; see our Terms of Use for details. Text written by some authors may be released under additional licenses or into the public domain. See https://dumps.wikimedia.org/legal.html for details.
DOI: 10.5281/zenodo.15162182
title: Complete List of Mathematical Expressions in all Wikimedia Projects, including Wikipedia

description: This dataset contains a deduplicated list of all mathematical expressions used in all wikimedia projects. The data is provided as json file where the key is the md5 hash of the input. The input is what was extracted from the wikitext sources. This was done in the following way:



All current dump were filtered for the math tag (see https://doi.org/10.5281/zenodo.15107679) for details

Those dumps were imported into a mediawiki installation with the MathSearch extension. Here one database was used per wiki.

The data from all the mathlog tables were combined in one table, which was exported into a json file. The json contains a list of key value pairs where the keys are the md5 hashes of the input.


The scripts are available from

swh:1:cnt:faec2206a154db5a2711791f4211097e36bf1413; origin=https://github.com/MaRDI4NFDI/wikiFilter; visit=swh:1:snp:28ed43d0e16ca3d6ce4bad1b484cec9d1124cd48; anchor=swh:1:rev:855735a5c90a0db3ccfd20c3899af4c82bc6704f; path=/wmcloud/allFormulae.sqlExample: The Wikipedia article on mass energy equivalence contains the following wikitext



<math qid=Q35875>E = mc^2</math>


the MathSearch extension extracts the user input



E = mc^2


the md5 hash is 



281a70c20b16a38d7781189936e1ac9f


and thus the row



    "281a70c20b16a38d7781189936e1ac9f": "E = mc^2",


in the json file corresponds to that input.
description: Except as discussed below, all original textual content is licensed under the  GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 4.0 License. Some text may be available only under the Creative Commons license; see our Terms of Use for details. Text written by some authors may be released under additional licenses or into the public domain. See https://dumps.wikimedia.org/legal.html for details.
DOI: 10.15468/dl.qsgxsv
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "DatasetKey is Birds and Mammals Collections of the Zoological Museum of M.V. Lomonosov Moscow State University",
 "Geometry POLYGON((51.41016 41.67697,80.21746 39.90693,90.70604 47.72965,76.70727 56.8242,61.53571 56.96751,50.88249 52.66492,44.26649 49.30824,51.41016 41.67697))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ellobius Fischer, 1814"
 ]
}

 The dataset includes 48 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006986-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7e558
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((51.41016 41.67697,80.21746 39.90693,90.70604 47.72965,76.70727 56.8242,61.53571 56.96751,50.88249 52.66492,44.26649 49.30824,51.41016 41.67697))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ellobius Fischer, 1814"
 ]
}

 The dataset includes 66 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006987-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15162270
title: Programació Musical del Palau de la Música Catalana: Concerts i Repertori
DOI: 10.5281/zenodo.15162271
title: Programació Musical del Palau de la Música Catalana: Concerts i Repertori
DOI: 10.48577/jpl.afcrtc
title: PRIMA: 1/f Noise and Mitigating Observing Strategies

description: No abstract available.
DOI: 10.5281/zenodo.15162274
title: The Digital Catalyst: Exploring How AI, Gamified Learning, and Social Platforms Fuel Gen Z's Academic Motivation

description: Abstract— The purpose of this study is to examine how learning motivation in Generation Z is affected by gamification, digital social interaction, and artificial intelligence (AI)-based educational technology. Additionally, it seeks to determine whether the three factors taken together have a greater effect than each one used alone. The growing use of digital technology in education and Gen Z's propensity for technological adaptation, such as their preference for social and interactive learning environments, are the driving forces behind this study. The Structural Equation Modeling Partial Least Squares (SEM-PLS) method is used in this study's quantitative methodology. 370 active students from different Indonesian universities who had participated in a digital technology-based learning process made up the research sample. A 5-point Likert scale was used in an online survey to collect data. The findings indicate that learning motivation is positively and significantly impacted by the three independent variables, namely AI, gamification, and digital social interaction. The coefficient values for AI, gamification, and social interaction are β = 0.425, β = 0.346, and β = 0.236, respectively, and are all significant at p < 0.001. The model can account for 81.6% of the variance in learning motivation, according to the R2 value of 0.816. It has been shown that the combination of these three elements has a greater effect than their individual application. In order to optimize the motivational potential of educational technology for Generation Z, this study recommends the creation of an integrative and adaptive digital learning system.

Key words: Digital social interaction, gamification, and artificial intelligence
DOI: 10.5281/zenodo.15162275
title: The Digital Catalyst: Exploring How AI, Gamified Learning, and Social Platforms Fuel Gen Z's Academic Motivation

description: Abstract— The purpose of this study is to examine how learning motivation in Generation Z is affected by gamification, digital social interaction, and artificial intelligence (AI)-based educational technology. Additionally, it seeks to determine whether the three factors taken together have a greater effect than each one used alone. The growing use of digital technology in education and Gen Z's propensity for technological adaptation, such as their preference for social and interactive learning environments, are the driving forces behind this study. The Structural Equation Modeling Partial Least Squares (SEM-PLS) method is used in this study's quantitative methodology. 370 active students from different Indonesian universities who had participated in a digital technology-based learning process made up the research sample. A 5-point Likert scale was used in an online survey to collect data. The findings indicate that learning motivation is positively and significantly impacted by the three independent variables, namely AI, gamification, and digital social interaction. The coefficient values for AI, gamification, and social interaction are β = 0.425, β = 0.346, and β = 0.236, respectively, and are all significant at p < 0.001. The model can account for 81.6% of the variance in learning motivation, according to the R2 value of 0.816. It has been shown that the combination of these three elements has a greater effect than their individual application. In order to optimize the motivational potential of educational technology for Generation Z, this study recommends the creation of an integrative and adaptive digital learning system.

Key words: Digital social interaction, gamification, and artificial intelligence
DOI: 10.48577/jpl.dc7mxd
title: Endurance

description: No abstract available.
DOI: 10.48577/jpl.1qqeg8
title: Recent Results at Mars from NASA's Mars Exploration Program

description: No abstract available.
DOI: 10.48577/jpl.goaxhw
title: Ocean Worlds Primer

description: No abstract available.