cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.dncjsxm6z
title: Dataset 1 of 8: Daily nitrogen dioxide concentrations in California (2012-2015) (CARB 19RD004)

description: This dataset includes daily nitrogen dioxide (NO2) concentrations across
 California from 2012 to 2015, with a spatial resolution of 100 meters. It
 is part of a comprehensive air pollution dataset generated through CARB
 funding: 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 1 of 8: Daily nitrogen dioxide concentrations in California
 (2012-2015) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.dncjsxm6z](https://doi.org/10.5061/dryad.dncjsxm6z) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section ([https://doi.org/10.5061/dryad.6djh9w18p).](https://doi.org/10.5061/dryad.6djh9w18p\).)
DOI: 10.5061/dryad.j3tx95xp4
title: Dataset 2 of 8: Daily nitrogen dioxide concentrations in California (2016-2019) (CARB 19RD004)

description: This dataset includes daily NO2 concentrations across California from 2016
 to 2019, at a spatial resolution of 100 meters. It is part of a
 comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 2 of 8: Daily nitrogen dioxide concentrations in California
 (2016-2019) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.j3tx95xp4](https://doi.org/10.5061/dryad.j3tx95xp4) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.5061/dryad.5qfttdzf6
title: Dataset 3 of 8: Particulate matter PM2.5 concentrations in California (2012-2013) (CARB 19RD004)

description: This dataset contains daily PM2.5 concentrations across California from
 2012 to 2015, with a spatial resolution of 100 meters. It is part of a
 comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 3 of 8: Particulate matter PM2.5 concentrations in California
 (2012-2013) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.5qfttdzf6](https://doi.org/10.5061/dryad.5qfttdzf6) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.5061/dryad.9w0vt4bpx
title: Dataset 5 of 8: Particulate matter PM2.5 concentrations in California (2016-2017) (CARB 19RD004)

description: This dataset includes daily PM2.5 concentrations across California from
 2016 to 2017, at a spatial resolution of 100 meters. It is part of a
 comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 5 of 8: Particulate matter PM2.5 concentrations in California
 (2016-2017) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.9w0vt4bpx](https://doi.org/10.5061/dryad.9w0vt4bpx) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.5061/dryad.jsxksn0jh
title: Dataset 7 of 8: Ozone (O3) concentrations in California (2012-2015) (CARB 19RD004)

description: This dataset contains daily ozone (O3) concentrations across California
 from 2012 to 2015, with a spatial resolution of 100 meters. It is part of
 a comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 7 of 8: Ozone (O3) concentrations in California (2012-2015)
 (CARB 19RD004)
 [https://doi.org/10.5061/dryad.jsxksn0jh](https://doi.org/10.5061/dryad.jsxksn0jh) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.5061/dryad.0vt4b8h6h
title: Dataset 8 of 8: Ozone (O3) concentrations in California (2016-2019) (CARB 19RD004)

description: This dataset contains daily ozone (O3) concentrations across California
 from 2016 to 2019, at a spatial resolution of 100 meters. It is part of a
 comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 8 of 8: Ozone (O3) concentrations in California (2016-2019)
 (CARB 19RD004)
 [https://doi.org/10.5061/dryad.0vt4b8h6h](https://doi.org/10.5061/dryad.0vt4b8h6h) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.7266/ns712x35
title: Project results from "Engaging New Orleanians in community-based flood monitoring" project
DOI: 10.6084/m9.figshare.31173670
title: <b>Warming drives soil carbon loss by alleviating microbial carbon limitation</b> <b>in the Tibetan Plateau</b>

description: <b>Warming drives soil carbon loss by alleviating microbial carbon limitation</b> <b>in the Tibetan Plateau</b>
DOI: 10.6084/m9.figshare.31173670.v1
title: <b>Warming drives soil carbon loss by alleviating microbial carbon limitation</b> <b>in the Tibetan Plateau</b>

description: <b>Warming drives soil carbon loss by alleviating microbial carbon limitation</b> <b>in the Tibetan Plateau</b>
DOI: 10.5061/dryad.wm37pvmw6
title: Dataset 4 of 8: Particulate matter PM2.5 concentrations in California (2014-2015) (CARB 19RD004)

description: This dataset includes daily fine particulate matter (PM2.5) concentrations
 across California from 2014 to 2015, with a spatial resolution of 100
 meters. It is part of a comprehensive air pollution dataset generated
 through CARB funding: 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 4 of 8: Particulate matter PM2.5 concentrations in California
 (2014-2015) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.wm37pvmw6](https://doi.org/10.5061/dryad.wm37pvmw6) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.5061/dryad.h70rxwds5
title: Dataset 6 of 8: Particulate matter PM2.5 concentrations in California (2018-2019) (CARB 19RD004)

description: This dataset includes daily PM2.5 concentrations across California from
 2018 to 2019, at a spatial resolution of 100 meters. It is part of a
 comprehensive air pollution dataset generated through CARB funding:
 19RD004.
description: This dataset was collected and processed as detailed in our
 Science Advances paper: Jason G. Su, Vy Vuong, Eahsan
 Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael
 Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics
 in Disadvantaged Communities through High-Resolution Mapping."
 Science Advances, https://doi.org/10.1126/sciadv.adm9986.
description: # Dataset 6 of 8: Particulate matter PM2.5 concentrations in California
 (2018-2019) (CARB 19RD004)
 [https://doi.org/10.5061/dryad.h70rxwds5](https://doi.org/10.5061/dryad.h70rxwds5) For comprehensive details regarding the data's description, file structure, sharing/access protocols, data sources, and associated code/software, please refer to the readme file located in the general section at [https://doi.org/10.5061/dryad.6djh9w18p](https://doi.org/10.5061/dryad.6djh9w18p)
DOI: 10.6084/m9.figshare.31165438
title: Future Readiness Index: Leadership Confidence vs Capability Gap Report 2026

description: Analyzes the disconnect between leadership self-assessment and actual capability metrics.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165438.v1
title: Future Readiness Index: Leadership Confidence vs Capability Gap Report 2026

description: Analyzes the disconnect between leadership self-assessment and actual capability metrics.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165432
title: Future Readiness Index: Innovation Readiness Report 2026

description: Evaluates the ability to generate, test, and scale new ideas.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165432.v1
title: Future Readiness Index: Innovation Readiness Report 2026

description: Evaluates the ability to generate, test, and scale new ideas.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.25934/pr00012562.0
title: Available datapackage for study 'Early Detection, Containment, and Management of COVID-19 in Dialysis Facilities Using Multi-Modal Data Sources'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.6084/m9.figshare.31165429
title: Future Readiness Index: Execution Readiness Report 2026

description: Measures how effectively organizations convert intent into action.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165429.v1
title: Future Readiness Index: Execution Readiness Report 2026

description: Measures how effectively organizations convert intent into action.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165426
title: Future Readiness Index: Execution vs Awareness Gap Report 2026

description: Examines the disconnect between organizational awareness of disruption and actual execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165426.v1
title: Future Readiness Index: Execution vs Awareness Gap Report 2026

description: Examines the disconnect between organizational awareness of disruption and actual execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165423
title: Future Readiness Index: Engagement Readiness Report 2026

description: Measures how effectively organizations align people, customers, and partners around purpose.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165423.v1
title: Future Readiness Index: Engagement Readiness Report 2026

description: Measures how effectively organizations align people, customers, and partners around purpose.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165420
title: Future Readiness Index: Decision Half-Life Report 2026

description: Measures how long it takes an organization to move from decision to execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165420.v1
title: Future Readiness Index: Decision Half-Life Report 2026

description: Measures how long it takes an organization to move from decision to execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165414
title: Future Readiness Index: Culture Readiness Report 2026

description: Measures how adaptable organizational norms and behaviors are during sustained change.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165414.v1
title: Future Readiness Index: Culture Readiness Report 2026

description: Measures how adaptable organizational norms and behaviors are during sustained change.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165411
title: Future Readiness Index: Collaboration Readiness Report 2026

description: Evaluates how effectively teams work across functions, hierarchies, and partners.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165411.v1
title: Future Readiness Index: Collaboration Readiness Report 2026

description: Evaluates how effectively teams work across functions, hierarchies, and partners.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165405
title: Future Readiness Index: Change Fatigue Report 2026

description: Measures the organizational impact of continuous change initiatives on workforce capacity and execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165405.v1
title: Future Readiness Index: Change Fatigue Report 2026

description: Measures the organizational impact of continuous change initiatives on workforce capacity and execution.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165402
title: Future Readiness Index: AI Readiness vs AI Hype Gap Report 2026

description: Assesses the gap between AI enthusiasm and actual organizational readiness to deploy and scale AI.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165402.v1
title: Future Readiness Index: AI Readiness vs AI Hype Gap Report 2026

description: Assesses the gap between AI enthusiasm and actual organizational readiness to deploy and scale AI.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165399
title: Future Readiness Index: 2026 Global Future Readiness Index

description: Measures organizational readiness across seven signals: Engagement, Learning, Collaboration, Culture, Innovation, Execution, and People.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.6084/m9.figshare.31165399.v1
title: Future Readiness Index: 2026 Global Future Readiness Index

description: Measures organizational readiness across seven signals: Engagement, Learning, Collaboration, Culture, Innovation, Execution, and People.<br><br>Author: Ian Khan, Chief Futurist, The Futurist Research Labs. Part of the Future Readiness Index Series 2026.
DOI: 10.15468/dl.ubg6ng
title: Occurrence Download

description: A dataset containing 5761 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "GadmGid is BRA.24_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Anura"
 ]
}

 The dataset includes 5761 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003977-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14655514
title: Market Prices of Commodities in South Tangerang
DOI: 10.15468/dl.5tpbkb
title: Occurrence Download

description: A dataset containing 1960 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "StateProvince is Tamaulipas",
 "TaxonKey is Dryobates scalaris",
 "Year 2001-2026"
 ]
}

 The dataset includes 1960 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003979-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ransbx
title: Occurrence Download

description: A dataset containing 16001 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Graphium sarpedon"
 ]
}

 The dataset includes 16001 records from 175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003978-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mhg66h
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Brachyhypopomus bennetti",
 "Year 2016-2026"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003980-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.73n5tb34q
title: Data from: Defining metabolic flexibility in hair follicle stem cell induced squamous cell carcinoma

description: Among the numerous changes associated with the transformation to cancer,
 cellular metabolism is one of the first discovered and most prominent.
 However, despite the knowledge that nearly every cancer is associated with
 the strong upregulation of various metabolic pathways, there has yet to be
 much clinical progress on the treatment of cancer by targeting a single
 metabolic enzyme directly. We previously showed that inhibition of
 glycolysis through lactate dehydrogenase (LDHA) deletion in cancer cells
 of origin had no effect on the initiation or progression of cutaneous
 squamous cell carcinoma, suggesting that these cancers are metabolically
 flexible enough to produce the necessary metabolites required for
 sustained growth in the absence of glycolysis.  Here we focused
 on glutaminolysis, another metabolic pathway frequently implicated as
 important for tumorigenesis in correlative studies. We genetically blocked
 glutaminolysis through glutaminase (GLS) deletion in cancer cells of
 origin, and found that this had little effect on tumorigenesis, similar to
 what we previously showed for blocking glycolysis. Tumors with genetic
 deletion of glutaminolysis instead upregulated lactate consumption and
 utilization for the TCA cycle, providing further evidence of metabolic
 flexibility. We also found that the metabolic flexibility observed upon
 inhibition of glycolysis, pyruvate oxidation, or glutaminolysis is due to
 post-transcriptional changes in the levels of plasma membrane lactate,
 glucose, and glutamine transporters. To define the limits of metabolic
 flexibility in cancer initiating hair follicle stem cells, we genetically
 blocked both glycolysis and glutaminolysis simultaneously and found that
 frank carcinoma was not compatible with abrogation of both of these carbon
 utilization pathways.  These data point towards metabolic
 flexibility mediated by regulation of nutrient consumption, and suggest
 that treatment of cancer through metabolic manipulation will require
 multiple interventions on distinct pathways.
description: # Data from: Defining metabolic flexibility in hair follicle stem cell
 induced squamous cell carcinoma
 [https://doi.org/10.5061/dryad.73n5tb34q](https://doi.org/10.5061/dryad.73n5tb34q) For **RNAseq**, wild-type and knock out are labeled on respective files. For **metabolomics**, data is organized based on the carbon labeled used, pool data, and genotype if indicated on sheet tab titles. Wild-type and knock out samples are labeled on the sheet itself or on the sheet title. Empty cells and "NA" labeled cells are values that were not detected for metabolites.  * pool = metabolic pool levels  * gls = glutaminase wt vs ko  * label = carbon 13 isotope labeling  * ldha = lactate dehydrogenase * mpc = mitochondrial pyruvate carrier  * scc = squamous cell carcinoma