cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/6zmv2-asp61
title: E_coli_EPEC_8612

description: The aim is to look for bacterial nanotubes (See Pal et al., 2019 Cell)
description: <strong>Tilt Series Date:</strong> 2020-02-13</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> 8612, EPEC Δrof1-escLΔrof3-mpcΔescN-espFΔflhBAΔfliOPQRΔminCD</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 105.0 eV/Å²,  defocus: -6.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> These cells were obtained from Ritesh (Ilan Rosenshine lab) and Amit (Sigal ben Yehuda lab, . both from the Hebrew university). They were present during sample preparation.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> An overnight culture from -80C stock was made in 2ml LB and was grown at 37C in static . conditions. The next day, 1 ml of cells were spun down at 4000 rpm for six minutes and . concentrated 10 times. 3 ul of cells were spotted on glow-discharged grids located on a . DMEM high-glucose agar plates (1.5% agar) for 3 hours. Then, the grids were washed twice . in PBS and transferred to a starvation-medium agar plates (Starvation medium:DMEM without . phenol red, vitamins and amino acids). and incubated for 1 hour at 37C. Subsequently,the . samples were washed with PBS and plunge-frozen. </p>
<strong>Sample Preparation:</strong> The samples were hold with a tweezer from the starvation plates and washed with PBS. Blotted . with different conditions (B</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-02-13-48/rawdata/</p>EPEC_8612_024_af.mrc,  Tilt Series (Pixel Size 0.449 nm), 1.9 GB  
        
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        EPEC_8612_024_af_full.rec,  Reconstruction (Pixel Size 1.796 nm), 737.3 MB  
        
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DOI: 10.22002/z50rz-qnm43
title: Vibrio cholerae SAD2090

description: Vibrio, T4P, competence pilus
description: <strong>Tilt Series Date:</strong> 2020-09-09</p>
<strong>Data Taken By:</strong> Stefan Kreida</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> SAD2090</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -8.7 μm,  magnification: 26000x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain from Ankur B Dalia, ankdalia@indiana.edu, PhD, Assistant Professor, Indiana University. . Cell growth & preparation, data collection and frame alignment by Stefan Kreida.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> 100 uL O/N preculture used to inoculate 3 mL LB, 20 mM MgSO4, 10 mM CaCl2, 100 uM IPTG. . Cells were grown for 7 h (30C) on 170 rpm. 100 uL culture was then spun down at 5,000 x g (5') . and resuspended in 100 uL aquarium salt (Instant Ocean), 7 g/L. </p>
<strong>Sample Preparation:</strong> BSA/10 nm gold suspension: 1 mL 10 nm gold suspension mixed with 0.25 mL 5% BSA, spun . at 14,000 rpm (10'). Supernatant removed so that 35 uL remained, which was used to . resuspend the gold particles. 30 uL cells mixed with 5 uL gold suspension. 3 uL sample . applied in Vitrobot at 100% humidity. Grid: Quantifoil R2/2, Cu NH2, LotID 186865.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/skv2020-09-09-13/rawdata/</p>G12_SAD2090_TS21_ali.mrc,  Tilt Series (Pixel Size 0.33 nm), 2.9 GB  
        
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        G12_SAD2090_TS21_ali_part61_4.rec,  Reconstruction (Pixel Size 1.32 nm), 737.3 MB  
        
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DOI: 10.22002/dz16g-tgp69
title: H_pylori_dFliO_GC

description: These are fast tilt series taken by Georges Chreifi on the Krios for dFliO mutant of Helicobacter  .  pylori.
description: <strong>Tilt Series Date:</strong> 2019-10-25</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Helicobacter pylori</p>
<strong>Strain:</strong> 26695</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 130.0 eV/Å²,  defocus: -6.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> I received these cells from Carrie Schaffer from the university of Kentucky. These are wild . . type cells but they don't express complete FliP protein. Therefore, they don't assemble full . . flagella. We knocked out FliO protein of the flagellar motor to study the early assembly states . of flagellar motor.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> I grew the cells on blood agar plates from -80 stock at 37C with 5% CO2. the cells were grown . . for 48 hours and then spread on a new plate with a cotton swab and left to grow for 24 hours. . . The grids were prepared from both batches (48 hours, and 24 hours, separately).</p>
<strong>Sample Preparation:</strong> The samples were prepared with Vitrobot IV. Blotting conditions were either: blot force 6, blot . . time 3, drain time 1 OR blot force -3, blot time 4, drain time 1, 100% humidity. Cu grids. 10 nm . . gold.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2019-10-25-220/rawdata/</p>Hpylori_FliO_225_af.mrc,  Tilt Series (Pixel Size 0.449 nm), 3.9 GB  
        
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        Hpylori_FliO_225_af_full.rec,  Reconstruction (Pixel Size 1.796 nm), 737.3 MB  
        
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DOI: 10.22002/hvg5q-5em27
title: H-pylori_dFliM_GC

description: These are fast tilt series taken by Georges Chreifi on the Krios for dFliM mutant of Helicobacter  .  pylori.
description: <strong>Tilt Series Date:</strong> 2019-10-28</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Helicobacter pylori</p>
<strong>Strain:</strong> 26695</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 130.0 eV/Å²,  defocus: -6.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> I received these cells from Carrie Schaffer from the university of Kentucky. These are wild type cells but they . don't express complete FliP protein. Therefore, they don't assemble full flagella. We knocked out FliM protein . of the flagellar motor to study the early assembly states of flagellar motor.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> I grew the cells on blood agar plates from -80 stock at 37C with 5% CO2. the cells were grown . for 48 hours and then spread on a new plate with a cotton swab and left to grow for 24 hours. . The grids were prepared from both batches (48 hours, and 24 hours, separately).</p>
<strong>Sample Preparation:</strong> The samples were prepared with Vitrobot IV. Blotting conditions were either: blot force 6, blot . time 3, drain time 1 OR blot force -3, blot time 4, drain time 1, 100% humidity. Cu grids. 10 nm . gold.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2019-10-28-65/rawdata/</p>Hpylori_FliM_067_af.mrc,  Tilt Series (Pixel Size 0.449 nm), 3.9 GB  
        
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        Hpylori_FliM_067_af_full.rec,  Reconstruction (Pixel Size 1.796 nm), 737.3 MB  
        
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        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.tz47h4
title: Occurrence Download

description: A dataset containing 8234 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alternanthera brasiliana"
 ]
}

 The dataset includes 8234 records from 327 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006341-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ywh2mz
title: Occurrence Download

description: A dataset containing 5614572 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris)"
 ]
}

 The dataset includes 5614572 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003357-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rasrxy
title: Occurrence Download

description: A dataset containing 92 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Protura",
 "Year is one of (1999, 2000, 2001, 2005, 2006, 2007, 2011, 2012, 2013, 2017, 2018, 2019)",
 "Continent is Europe",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Occurrence evidence, Observation)"
 ]
}

 The dataset includes 92 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003381-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16980444
title: Toward a Framework for Behavioral Assurance in AI: Integrating SCAB and PRIS for Risk Mitigation

description: Artificial intelligence (AI) systems are increasingly integrated into everyday life, influencing education, healthcare, governance, and personal decision-making. Yet, existing security and assurance frameworks, including those from the National Institute of Standards and Technology (NIST) and ISO/IEC, were developed for traditional data-centric threats and remain inadequate for behavioral and cognitive risks posed by generative AI and autonomous agents. This paper introduces the SCAB Protocol (Sovereignty, Coherence, Alignment, Boundaries, Agency, Probity) and the PRIS Protocol (Psychosis Risk Interaction Score) as complementary frameworks to address these risks. SCAB establishes system-level behavioral assurance by embedding six domains of ethical and operational safeguards, while PRIS provides runtime psychosis-risk evaluation of human–AI interactions. The combined framework expands traditional information assurance toward behavioral assurance, offering a dual-layer defense against emergent harms. Drawing on parallels to information assurance models in the Internet of Things (IoT), this paper introduces the concept of Behavioral Assurance Profiles (BAPs) for sector-specific applications. Use cases are presented in education, healthcare, governance, and public safety. The findings underscore the urgency of adopting behavioral assurance as a core principle in AI governance and call for the development of standards and certification regimes akin to cybersecurity frameworks but tailored to cognitive safety.
DOI: 10.5281/zenodo.16980445
title: Toward a Framework for Behavioral Assurance in AI: Integrating SCAB and PRIS for Risk Mitigation

description: Artificial intelligence (AI) systems are increasingly integrated into everyday life, influencing education, healthcare, governance, and personal decision-making. Yet, existing security and assurance frameworks, including those from the National Institute of Standards and Technology (NIST) and ISO/IEC, were developed for traditional data-centric threats and remain inadequate for behavioral and cognitive risks posed by generative AI and autonomous agents. This paper introduces the SCAB Protocol (Sovereignty, Coherence, Alignment, Boundaries, Agency, Probity) and the PRIS Protocol (Psychosis Risk Interaction Score) as complementary frameworks to address these risks. SCAB establishes system-level behavioral assurance by embedding six domains of ethical and operational safeguards, while PRIS provides runtime psychosis-risk evaluation of human–AI interactions. The combined framework expands traditional information assurance toward behavioral assurance, offering a dual-layer defense against emergent harms. Drawing on parallels to information assurance models in the Internet of Things (IoT), this paper introduces the concept of Behavioral Assurance Profiles (BAPs) for sector-specific applications. Use cases are presented in education, healthcare, governance, and public safety. The findings underscore the urgency of adopting behavioral assurance as a core principle in AI governance and call for the development of standards and certification regimes akin to cybersecurity frameworks but tailored to cognitive safety.
DOI: 10.34894/ov1ki8
title: 25.099 Haghorst - De Welder 2

description: Bij archeologisch onderzoek op de nieuwbouwlocatie “De Welder 2” in Haghorst is een vermoedelijk middeleeuwse boomstamput aangetroffen. Hiervan is een fragment aangeleverd voor onderzoek. Dendrochronologisch onderzoek van het fragment leverde geen overtuigende resultaten op, waardoor de ouderdom niet verder aangescherpt kan worden.
DOI: 10.6084/m9.figshare.30000964
title: AI-based research mentors: Plausible scenarios and ethical issues

description: Guided by Brey’s Anticipatory Technology Ethics, we examined AI-based research mentors (AIRMs) through technology foresight as well as identification and evaluation of ethical issues. Scenario planning was employed to inform foresight, yielding four plausible future scenarios: 1) AIRMs are used solely for guidance, 2) AIRMs are used for guidance and monitoring, 3) AIRMs are banned, and 4) AIRMs are used solely for monitoring. Resnik’s twelve principles informed the identification of ethical issues within these scenarios. Our analysis revealed that certain principles – openness, education, legality, and mutual respect – were violated in all scenarios. Others were contravened to varying degrees across the scenarios; for example, freedom was only violated in scenarios where AIRMs were used for monitoring. Furthermore, the guidance scenario showed that AIRM’s responses could be manipulated to justify poor practice (“AIRMing”). In our evaluation, we weighed ethical issues against the benefits and found that the guidance-only scenario was the least problematic. While this scenario has benefits, such as providing expert guidance on research, ethical issues arise with regard to honesty, openness, credit, education, legality, and mutual respect. Therefore, policy must be developed to ensure that AIRMs are used solely for guidance while mitigating these issues.
DOI: 10.6084/m9.figshare.30000964.v1
title: AI-based research mentors: Plausible scenarios and ethical issues

description: Guided by Brey’s Anticipatory Technology Ethics, we examined AI-based research mentors (AIRMs) through technology foresight as well as identification and evaluation of ethical issues. Scenario planning was employed to inform foresight, yielding four plausible future scenarios: 1) AIRMs are used solely for guidance, 2) AIRMs are used for guidance and monitoring, 3) AIRMs are banned, and 4) AIRMs are used solely for monitoring. Resnik’s twelve principles informed the identification of ethical issues within these scenarios. Our analysis revealed that certain principles – openness, education, legality, and mutual respect – were violated in all scenarios. Others were contravened to varying degrees across the scenarios; for example, freedom was only violated in scenarios where AIRMs were used for monitoring. Furthermore, the guidance scenario showed that AIRM’s responses could be manipulated to justify poor practice (“AIRMing”). In our evaluation, we weighed ethical issues against the benefits and found that the guidance-only scenario was the least problematic. While this scenario has benefits, such as providing expert guidance on research, ethical issues arise with regard to honesty, openness, credit, education, legality, and mutual respect. Therefore, policy must be developed to ensure that AIRMs are used solely for guidance while mitigating these issues.
DOI: 10.15468/dl.yeztv3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vitis vulpina",
 "Country is Armenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003385-250827131500795/datasets/export for details.
DOI: 10.15468/dl.andc3h
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vitis vulpina",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003386-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dujcwk
title: Occurrence Download

description: A dataset containing 220528 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heracleum mantegazzianum"
 ]
}

 The dataset includes 220528 records from 581 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003339-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3razvu
title: Occurrence Download

description: A dataset containing 460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Elephas maximus indicus",
 "Year 2023-2025"
 ]
}

 The dataset includes 460 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003383-250827131500795/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/12894620
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.15468/dl.h3t8ah
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carex mandshurica"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003388-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5k8fbd
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vitis vulpina",
 "Country is Hungary",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003387-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16979883
title: Blue Carbon Projects Database — Supplement to Farahmand et al. (2025)

description: This Excel dataset accompanies the article:

Farahmand, S., Hilmi, N. & Duarte, C.M. (2025). The rise and flows of blue carbon credits advance global climate and biodiversity goals. npj Ocean Sustainability, 4, 39. https://doi.org/10.1038/s44183-025-00141-6

The dataset compiles structured information on global Blue Carbon Projects (BCPs), their issued Blue Carbon Credits (BCCs), estimated greenhouse gas (GHG) reductions, and buyers. Data are extracted from official registries and public records.

The Excel file contains five sheets:- README: Documentation of dataset structure and variables.- BCPs: Information on 94 projects across registries (ACR, CAR, Plan Vivo, VCS, Japan’s BCPs, ACCU).- BCCs: Annual issuance, retirement, and remaining balance of Blue Carbon Credits (2011–2025).- Estimated GHG Reduction: Lifetime emission reduction estimates per project.- BCC Buyers: Information on buyers, including buyer type, country, and purchased credits.

🧮 A distinctive feature of this dataset is the use of built-in formulas and structured filters in the Excel file. Users can dynamically filter projects by registry, country, or other attributes, and totals (issued, retired, remaining credits) are automatically recalculated. This design makes the dataset not only a data repository but also an interactive analytical tool for researchers and practitioners.