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

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-112.7688 29.24913, -112.6188 29.24913, -112.6188 29.39913, -112.7688 29.39913, -112.7688 29.24913))"
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046010-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdme86
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Saguinus imperator"
 ]
}

 The dataset includes 307 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046011-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.susmz3
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Saguinus labiatus rufiventer"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046012-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28821194
title: Pathways to a successful investment fund centre

description: We use fsQCA to detect which of the 3 conditions of the combination thereof lead to a success investment fund centre
DOI: 10.6084/m9.figshare.28821194.v7
title: Pathways to a successful investment fund centre

description: We use fsQCA to detect which of the 3 conditions of the combination thereof lead to a success investment fund centre
DOI: 10.6084/m9.figshare.28821194.v6
title: Pathways to a successful investment fund centre

description: We use fsQCA to detect which of the 3 conditions of the combination thereof lead to a success investment fund centre
DOI: 10.15468/dl.s3w6hr
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Saguinus labiatus labiatus"
 ]
}

 The dataset includes 43 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046013-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7w3gwm
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-112.6188 29.24913, -112.4688 29.24913, -112.4688 29.39913, -112.6188 29.39913, -112.6188 29.24913))"
 ]
}

 The dataset includes 175 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046014-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17653433
title: Solar Wind Predictions at Jupiter and Mars

description: Measurements of the solar wind conditions upstream of planetary magnetosphere are typically not available concurrently with in situ magnetospheric measurements. Therefore, we must rely on models that propagate solar wind data measured at the Earth’s orbit at 1 AU out to planetary orbits, including Jupiter’s orbit at 5.2 AU. This dataset contains model output files from a 2-D multi-fluid MHD solar wind propagation model, called SWMF-OH, that predict the solar wind conditions upstream of Jupiter during Juno’s prime and extended missions. It provides solar wind predictions at both Mars and Jupiter, for comparison with MAVEN and Juno in situ data, respectively, and at Juno’s position in the solar wind during its approach to Jupiter (2014-2016).

The SWMF-OH model was adapted by Boston University Senior Research Scientist Bertalan Zieger from the outer heliosphere (OH) component of the Space Weather Modeling Framework (SWMF) (Tóth et al., 2012), which is a 3-D global multi-fluid MHD model of the outer heliosphere with one ion fluid and four neutral populations (Opher et al., 2006; 2009). It provides time-dependent 2-D multi-fluid MHD simulations of solar wind propagation from a heliocentric distance of 1 AU up to 50 AU. Model inputs are hourly OMNI data of the measured solar wind density, velocity, magnetic field, and temperature. Model outputs include the solar wind density, plasma temperature, velocity, and all three components of the IMF.

The files include: year, month (integer number 1-12), day (integer number 1-31), hour, position in HCI cartesian coordinates, solar wind number density, solar wind velocity in RTN coordinates, interplanetary magnetic field in RTN coordinates, solar wind thermal pressure, and current density in RTN coordinates (see header line in each file for units).

A significant advantage of this model is that it can predict all three components of the interplanetary magnetic field. 1-D MHD models cannot propagate BR because of the divergence-free nature of the magnetic field (see discussion in Zieger and Hansen, 2008). Predicting the IMF is crucial for fully understanding the interaction between the solar wind and Jupiter’s magnetosphere, for example in determining whether the IMF orientation was favorable for dayside reconnection. Additionally, SWMF-OH does a good job of predicting intervals of increased solar wind PDyn from corotating interaction regions even when the Earth-Sun-Jupiter angle is large. Finally, it is useful to consider output from multiple models wherever possible, since we can have additional confidence in the predictions when results of different models agree.
DOI: 10.5281/zenodo.17653434
title: Solar Wind Predictions at Jupiter and Mars

description: Measurements of the solar wind conditions upstream of planetary magnetosphere are typically not available concurrently with in situ magnetospheric measurements. Therefore, we must rely on models that propagate solar wind data measured at the Earth’s orbit at 1 AU out to planetary orbits, including Jupiter’s orbit at 5.2 AU. This dataset contains model output files from a 2-D multi-fluid MHD solar wind propagation model, called SWMF-OH, that predict the solar wind conditions upstream of Jupiter during Juno’s prime and extended missions. It provides solar wind predictions at both Mars and Jupiter, for comparison with MAVEN and Juno in situ data, respectively, and at Juno’s position in the solar wind during its approach to Jupiter (2014-2016).

The SWMF-OH model was adapted by Boston University Senior Research Scientist Bertalan Zieger from the outer heliosphere (OH) component of the Space Weather Modeling Framework (SWMF) (Tóth et al., 2012), which is a 3-D global multi-fluid MHD model of the outer heliosphere with one ion fluid and four neutral populations (Opher et al., 2006; 2009). It provides time-dependent 2-D multi-fluid MHD simulations of solar wind propagation from a heliocentric distance of 1 AU up to 50 AU. Model inputs are hourly OMNI data of the measured solar wind density, velocity, magnetic field, and temperature. Model outputs include the solar wind density, plasma temperature, velocity, and all three components of the IMF.

The files include: year, month (integer number 1-12), day (integer number 1-31), hour, position in HCI cartesian coordinates, solar wind number density, solar wind velocity in RTN coordinates, interplanetary magnetic field in RTN coordinates, solar wind thermal pressure, and current density in RTN coordinates (see header line in each file for units).

A significant advantage of this model is that it can predict all three components of the interplanetary magnetic field. 1-D MHD models cannot propagate BR because of the divergence-free nature of the magnetic field (see discussion in Zieger and Hansen, 2008). Predicting the IMF is crucial for fully understanding the interaction between the solar wind and Jupiter’s magnetosphere, for example in determining whether the IMF orientation was favorable for dayside reconnection. Additionally, SWMF-OH does a good job of predicting intervals of increased solar wind PDyn from corotating interaction regions even when the Earth-Sun-Jupiter angle is large. Finally, it is useful to consider output from multiple models wherever possible, since we can have additional confidence in the predictions when results of different models agree.
DOI: 10.15468/dl.t84ndr
title: Occurrence Download

description: A dataset containing 2044 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-112.4688 29.24913, -112.3188 29.24913, -112.3188 29.39913, -112.4688 29.39913, -112.4688 29.24913))"
 ]
}

 The dataset includes 2044 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046015-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9qmtbp
title: Occurrence Download

description: A dataset containing 11302245 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "Country is Brazil",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 11302245 records from 563 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045998-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28738190.v33
title: Syllogistic reasoning pdf v1-2-3-4.rar

description: Logic and being
DOI: 10.5281/zenodo.15293416
title: Generic OpenPSA Models: The Aralia Fault Tree Dataset

description: This repository includes fault trees from the Aralia dataset, in OpenPSA XML format. The goals of including these models are to:

- Serve as test cases for various PRA tools, including OpenPRA.- Familiarize users with different modeling approaches and formats.- Provide a reference for PRA model exchange.

The repository also maintains a `schema` for the models.

OpenPSA Model Exchange Format

The OpenPSA Model Exchange Format (MEF) represents about a decade of effort to develop a format independent of any specific quantification engine. The goal was to create a standard that researchers, corporations, and regulators could use collaboratively in an open environment.

Model Sources

Models were gathered from the SCRAM project.

Additional References



A. Rauzy, E. Châtelet, Y. Dutuit, and C. Bérenguer, “A practical comparison of methods to assess sum-of-products,” Reliability Engineering & System Safety, vol. 79, no. 1, pp. 33–42, Jan. 2003, doi: 10.1016/S0951-8320(02)00165-5.

E. M. Aras, “Enhancement Methodology for Probabilistic Risk Assessment Tools through Diagnostics, Optimization, and Parallel Computing,” Doctor of Philosophy, North Carolina State University, Raleigh, North Carolina, 2024. [Online]. Available: https://repository.lib.ncsu.edu/items/bb05f7f5-1cff-4beb-9312-331bc94b0b95
DOI: 10.5281/zenodo.15293417
title: Generic OpenPSA Models: The Aralia Fault Tree Dataset

description: This repository includes fault trees from the Aralia dataset, in OpenPSA XML format. The goals of including these models are to:

- Serve as test cases for various PRA tools, including OpenPRA.- Familiarize users with different modeling approaches and formats.- Provide a reference for PRA model exchange.

The repository also maintains a `schema` for the models.

OpenPSA Model Exchange Format

The OpenPSA Model Exchange Format (MEF) represents about a decade of effort to develop a format independent of any specific quantification engine. The goal was to create a standard that researchers, corporations, and regulators could use collaboratively in an open environment.

Model Sources

Models were gathered from the SCRAM project.

Additional References



A. Rauzy, E. Châtelet, Y. Dutuit, and C. Bérenguer, “A practical comparison of methods to assess sum-of-products,” Reliability Engineering & System Safety, vol. 79, no. 1, pp. 33–42, Jan. 2003, doi: 10.1016/S0951-8320(02)00165-5.

E. M. Aras, “Enhancement Methodology for Probabilistic Risk Assessment Tools through Diagnostics, Optimization, and Parallel Computing,” Doctor of Philosophy, North Carolina State University, Raleigh, North Carolina, 2024. [Online]. Available: https://repository.lib.ncsu.edu/items/bb05f7f5-1cff-4beb-9312-331bc94b0b95
DOI: 10.15468/dl.dcgfdn
title: Occurrence Download

description: A dataset containing 2215 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alopias vulpinus (Bonnaterre, 1788)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2215 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003377-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15468712
title: New Generator

description: This dataset contains AI-generated from 6 diffusion model:



FLUX

Stable-Diffusion-3

Stable-Diffusion-XL

Firefly

Midjourney v5

DALLE3
DOI: 10.5281/zenodo.15468713
title: New Generator

description: This dataset contains AI-generated from 6 diffusion model:



FLUX

Stable-Diffusion-3

Stable-Diffusion-XL

Firefly

Midjourney v5

DALLE3
DOI: 10.15468/dl.3saew4
title: Occurrence Download

description: A dataset containing 2079895 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((93.63182 -1.84754,99.9007 -1.84754,99.9007 18.65339,93.63182 18.65339,93.63182 -1.84754))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2079895 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009202-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h29uvs
title: Occurrence Download

description: A dataset containing 116 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-88.08766 21.8052,-90.23684 21.46323,-90.81771 20.63201,-91.14686 19.43145,-92.57965 18.82779,-92.75391 18.38739,-92.07624 17.54017,-91.47602 17.00399,-90.79834 17.13356,-90.68217 17.614,-88.92022 17.63245,-87.48743 17.76157,-86.40316 20.26918,-86.75167 21.84115,-87.58424 22.02076,-88.08766 21.8052))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Astyanax altior Hubbs, 1936",
 "Year 2000-2025"
 ]
}

 The dataset includes 116 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011519-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jrq35h
title: Occurrence Download

description: A dataset containing 184797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Dama dama (Linnaeus, 1758)"
 ]
}

 The dataset includes 184797 records from 334 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011496-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/gdmurk
title: Passed Acts; St. 1843, c.69, SC1/series 229

description: &lt;p&gt;Petition subject: Great Massachusetts Petition &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:13448109"&gt;http://nrs.harvard.edu/urn-3:FHCL:13448109&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Rutland &lt;/p&gt; &lt;p&gt;Total signatures: 319 &lt;/p&gt; &lt;p&gt;Unidentified signatures: 319 &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: additional documents available &lt;/p&gt; &lt;p&gt;Additional archivist notes: George Latimer [note: this data reflects the information on the spreadsheet submitted by Henry I. Bowditch and William F. Channing to the legislature, which was filed with St. 1843, c.69, passed April 26, 1843. The majority of the Great Massachusetts Petition has not been located.] &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: St. 1843, c.69, passed April 26, 1843 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;
DOI: 10.7910/dvn/fw4psq
title: Wright Casebook Edited; Docketed Case: 73-1648
DOI: 10.7910/dvn/r9cr1o
title: Business analytics and decision science: A review of techniques in strategic business decision making

description: Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decisionmaking processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the important
DOI: 10.7910/dvn/l2qz3n
title: CES 2022, Team Module of National Cheng Kung University (NCK)

description: This dataverse contains the data and supporting documents for the CES 2022 Team Module of National Cheng Kung University. This project was supported by the National Science Foundation, Grant Number SES-2148907.
DOI: 10.7910/dvn/dbtqx
title: Senate Unpassed Legislation 1839, Docket 10525, SC1/series 231, Petition of Stillman Lothrop

description: &lt;p&gt;Petition subject: To abolish slavery in Washington D.C. and pass several resolutions against slavery, against the annexation of Texas or other slave states, repeal all laws making any distinctions on account of color, and right to trial by jury &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:11858093"&gt;http://nrs.harvard.edu/urn-3:FHCL:11858093&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Watertown &lt;/p&gt; &lt;p&gt;Legislator, committee, or address that the petition was sent to: Minot Thayer, Braintree; committee on slavery &lt;/p&gt; &lt;p&gt;Selected signatures:&lt;ol&gt;&lt;li&gt;Stillman Lothrop&lt;/li&gt;&lt;li&gt;Nathaniel Stone Jr.&lt;/li&gt;&lt;li&gt;Jesse Wheeler&lt;/li&gt;&lt;/ol&gt; &lt;/p&gt; &lt;p&gt;Actions taken on dates: 1839-03-01,1839-03-02 &lt;/p&gt; &lt;p&gt;Legislative action: Received in the House on March 1, 1839 and referred to the committee on slavery and sent for concurrence and received in the Senate on March 2, 1839 and concurred &lt;/p&gt; &lt;p&gt;Total signatures: 52 &lt;/p&gt; &lt;p&gt;Legislative action summary: Received, referred, sent, received, concurred &lt;/p&gt; &lt;p&gt;Legal voter signatures (males not identified as non-legal): 52 &lt;/p&gt; &lt;p&gt;Female only signatures: No &lt;/p&gt; &lt;p&gt;Identifications of signatories: inhabitants &lt;/p&gt; &lt;p&gt;Prayer format was &lt;a href="http://en.wikipedia.org/wiki/Printing"&gt;printed&lt;/a&gt; vs. &lt;a href="http://en.wikipedia.org/wiki/Manuscript"&gt;manuscript&lt;/a&gt;: Printed &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: no additional documents &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: Senate Unpassed 1839, Docket 10525 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;