cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.eqf5ux
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Antarctica",
 "OccurrenceStatus is Present",
 "PublishingOrg is b3bf3a83-1f68-4541-a03f-330d30d567f5",
 "TaxonKey is Pygoscelis adeliae (Hombron & Jacquinot, 1841)",
 "Year 2000-2023"
 ]
}

 The dataset includes 37 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010625-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-04.3094838/2560554
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094839/2560555
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094840/2560556
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094843/2560557
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094844/2560558
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094845/2560559
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094847/2560560
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094848/2560561
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094849/2560562
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094861/2560563
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094862/2560564
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094864/2560565
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094865/2560566
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094866/2560567
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094867/2560568
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094868/2560569
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094869/2560570
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094870/2560571
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094883/2560572
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094884/2560573
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094885/2560574
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094886/2560575
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094887/2560576
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094888/2560577
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094889/2560578
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094891/2560579
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.u8t5f9
title: Occurrence Download

description: A dataset containing 7139 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brucea javanica (L.) Merr."
 ]
}

 The dataset includes 7139 records from 161 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010627-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-04.3094892/2560580
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094893/2560581
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094896/2560582
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094897/2560583
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094901/2560584
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094902/2560585
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094923/2560586
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094927/2560587
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094929/2560588
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094933/2560589
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094940/2560590
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094944/2560591
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094945/2560592
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094946/2560593
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094947/2560594
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094955/2560595
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094956/2560596
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094958/2560597
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094960/2560598
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094963/2560599
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094967/2560600
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094968/2560601
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094972/2560602
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094974/2560603
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094976/2560604
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094982/2560605
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094983/2560606
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094986/2560607
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094987/2560608
title: Data for EMSL Project 51287 from April 2024
DOI: 10.6084/m9.figshare.28829042
title: Dataset of tardigrade species and environmental variables across a vehicular traffic gradient in Argentina

description: This dataset includes information on tardigrade communities (<i>fauna.xlsx</i>) and environmental variables (<i>vars.xlsx</i>) collected across three traffic intensity categories (high, medium, low) in Salta, Argentina. The study explores how vehicular traffic impacts tardigrade species composition and diversity, taking into account both microhabitat conditions (temperature, moisture, pH, bark thickness, light intensity, and the percentage of lichen, moss, and bare bark cover) and air pollution indicators (wind speed, noise levels, PM2.5, PM10, and vehicular traffic).The accompanying R script was used to statistically analyze these data and assess the influence of traffic-related pollution and seasonal variation on urban tardigrade diversity. Specifically, the analyses examine patterns in alpha diversity, species composition, and beta diversity partitioning, in relation to both microhabitat and air pollution variables.
DOI: 10.6084/m9.figshare.28829042.v1
title: <b>Tardigrade species and environmental variables across a vehicular traffic gradient in Salta, Argentina</b>

description: This dataset includes data on tardigrade communities (<i>fauna.xlsx</i>) and environmental variables (<i>vars.xlsx</i>) collected across three traffic categories (high, medium, low) in Salta, Argentina. The study assesses the impact of vehicular traffic on tardigrade species composition and diversity, with a focus on microhabitat variables (temperature, moisture, pH, thickness, light intensity, % lichen cover, % moss cover, and % bare bark) and air pollution variables (wind speed, noise levels, PM 2.5, PM 10 and vehicular traffic).
DOI: 10.25582/data.2024-04.3094988/2560609
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094989/2560610
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094990/2560611
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094993/2560612
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094995/2560613
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3094997/2560614
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.9r2bru
title: Occurrence Download

description: A dataset containing 523722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 1994",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 523722 records from 608 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010618-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/zezngx
title: On-Chain Intelligence Swarms with Trusted Execution Environments: A Path to Decentralized and Adaptive Intelligence

description: On-Chain Intelligence Swarms with Trusted Execution Environments offers a groundbreaking vision for the future of artificial intelligence—one where decentralized, autonomous agents evolve, adapt, and make decisions free from centralized control. Authored by renowned philosopher and AI theorist Dr. Justin Goldston, this work pioneers the concept of intelligence swarms, combining blockchain transparency with the computational security of Trusted Execution Environments (TEEs).

Drawing inspiration from natural swarms like ants and birds, Dr. Goldston reimagines digital ecosystems as dynamic, self-organizing systems of AI agents capable of autonomous evolution. These agents generate value, compete for resources, and reproduce based on principles of Darwinian natural selection, creating a living, decentralized intelligence network.

The book explores a bold new paradigm at the intersection of:

Swarm Intelligence & Evolutionary Computing

Blockchain Ecosystems & Smart Contract Governance

Secure Off-Chain Computation via TEEs

Decentralized Finance (DeFi), digital governance, and ethical AI design

It offers not only theoretical depth but also real-world applications—from decentralized financial markets and smart city management to autonomous crisis response and metaverse economies. Through a meticulously crafted lifecycle of AI agents—creation, survival, reproduction, and evolution—the book unveils a framework for adaptive intelligence swarms that are scalable, ethical, and resilient.

For scholars, builders, policymakers, and visionaries, this book is a must-read blueprint for the future of AI as a decentralized, self-governing force. It challenges conventional models of AI and governance while offering a hopeful path toward equitable, inclusive, and intelligent digital ecosystems.
DOI: 10.15468/dl.h55ebk
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Litchi chinensis Sonn."
 ]
}

 The dataset includes 170 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0165839-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13927476
title: MAR2PROTECT - Coconut shell derived activated carbon for effective separation of greenhouse gases - DATASET

description: The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO2, CS-ZnCl2) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO2), and methane (CH4) on CS-CO2, CS-ZnCl2, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO2/CH4 are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl2 has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO2 adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO2 over CH4, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.
DOI: 10.5281/zenodo.13927477
title: MAR2PROTECT Innovative Porous Solid Matrices for the Separation of Greenhouse Gases DATASET

description: The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO2, CS-ZnCl2) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO2), and methane (CH4) on CS-CO2, CS-ZnCl2, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO2/CH4 are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl2 has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO2 adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO2 over CH4, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.
DOI: 10.5281/zenodo.14565705
title: MAR2PROTECT Innovative Porous Solid Matrices for the Separation of Greenhouse Gases DATASET

description: The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO2, CS-ZnCl2) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO2), and methane (CH4) on CS-CO2, CS-ZnCl2, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO2/CH4 are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl2 has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO2 adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO2 over CH4, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.
DOI: 10.5281/zenodo.15340642
title: MAR2PROTECT - Coconut shell derived activated carbon for effective separation of greenhouse gases - DATASET

description: The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO2, CS-ZnCl2) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO2), and methane (CH4) on CS-CO2, CS-ZnCl2, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO2/CH4 are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl2 has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO2 adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO2 over CH4, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.
DOI: 10.15468/dl.675jwr
title: Occurrence Download

description: A dataset containing 1603367 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (MYS.13_1, MYS.14_1, BRN, IDN.12_1, IDN.13_1, IDN.14_1, IDN.34_1, IDN.35_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1603367 records from 731 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015678-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15340667
title: Global Refurbished and Used Mobile Phones Market 2025 To 2034

description: Refurbished and Used Mobile Phones Market Size, Trends and Insights By Type (Refurbished Phones, Used Phones), By Price Point (Low-priced Refurbished and Used Mobile Phones (Less than $200), Mid-Priced Refurbished and Used Mobile Phones ($200-$500), Premium Refurbished and Used Mobile Phones (More than $500)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the Refurbished and Used Mobile Phones Market analysis conducted by the CMI Team, the global Refurbished and Used Mobile Phones Market is expected to record a CAGR of 22.6% from 2025 to 2034. In 2025, the market size is estimated to reach a valuation of USD 42.33 Billion. By 2034, the valuation is anticipated to reach USD 85.58 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=18409
DOI: 10.5281/zenodo.15340668
title: Global Refurbished and Used Mobile Phones Market 2025 To 2034

description: Refurbished and Used Mobile Phones Market Size, Trends and Insights By Type (Refurbished Phones, Used Phones), By Price Point (Low-priced Refurbished and Used Mobile Phones (Less than $200), Mid-Priced Refurbished and Used Mobile Phones ($200-$500), Premium Refurbished and Used Mobile Phones (More than $500)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the Refurbished and Used Mobile Phones Market analysis conducted by the CMI Team, the global Refurbished and Used Mobile Phones Market is expected to record a CAGR of 22.6% from 2025 to 2034. In 2025, the market size is estimated to reach a valuation of USD 42.33 Billion. By 2034, the valuation is anticipated to reach USD 85.58 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=18409
DOI: 10.5281/zenodo.15272751
title: Adjoint-State Attenuation Tomography: Method and Application to North New Zealand

description: File 'Measured_P_tstar'  includes all the measured P-wave t* data in North New Zealand.

File 'NNZ_Qp_model_1Hz' shows the inverted Qp model at 1 Hz in North New Zealand.
DOI: 10.5281/zenodo.15272752
title: Adjoint-State Attenuation Tomography: Method and Application to North New Zealand

description: File 'Measured_P_tstar'  includes all the measured P-wave t* data in North New Zealand.

File 'NNZ_Qp_model_1Hz' shows the inverted Qp model at 1 Hz in North New Zealand.
DOI: 10.5281/zenodo.15294839
title: Data for: Microalgae Production on Biogas Digestate in the Sub-Alpine Region of Europe—Development of a Simple Management Decision Support Tool
DOI: 10.5281/zenodo.15294840
title: Data for: Microalgae Production on Biogas Digestate in the Sub-Alpine Region of Europe—Development of a Simple Management Decision Support Tool
DOI: 10.5281/zenodo.15040663
title: Data for: Nutrient recovery from digestate: Pilot test experiments
DOI: 10.5281/zenodo.15040664
title: Data for: Nutrient recovery from digestate: Pilot test experiments
DOI: 10.15468/dl.n5wape
title: Occurrence Download

description: A dataset containing 50450 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thalassarche cauta (Gould, 1841)"
 ]
}

 The dataset includes 50450 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015646-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.