cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/4x1zpd
title: Research Dataset of Content Creator (1995-2020)

description: The content creator research & publication dataset, which was indexed by Scopus between 1995 to 2020. The dataset contains 920 documents data: authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, correspondence address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
description: Scopus.com
DOI: 10.7910/dvn/k07y6h
title: Wright Files I-Z Edited; Docketed Case: 79-1063
DOI: 10.7910/dvn/kfrpe5
title: Passed Acts; St. 1862, c.44, SC1/series 229, Petition of William Ropes

description: &lt;p&gt;Petition subject: Incorporation &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:10520841"&gt;http://nrs.harvard.edu/urn-3:FHCL:10520841&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Massachusetts &lt;/p&gt; &lt;p&gt;Legislator, committee, or address that the petition was sent to: William B. May, Roxbury; committee on public charitable institutions &lt;/p&gt; &lt;p&gt;Selected signatures:&lt;ol&gt;&lt;li&gt;William Ropes&lt;/li&gt;&lt;li&gt;Albert Fearing&lt;/li&gt;&lt;li&gt;A.R. Thompson&lt;/li&gt;&lt;li&gt;A.M. Blasden&lt;/li&gt;&lt;li&gt;[George W. Blagden?]&lt;/li&gt;&lt;li&gt;James C. Dunn&lt;/li&gt;&lt;li&gt;Ezra S. Garrett&lt;/li&gt;&lt;li&gt;James Savagae&lt;/li&gt;&lt;li&gt;James Hayward&lt;/li&gt;&lt;li&gt;Thomas S. Mileau&lt;/li&gt;&lt;li&gt;B.C. Clark [Benjamin C. Clark]&lt;/li&gt;&lt;/ol&gt; &lt;/p&gt; &lt;p&gt;Actions taken on dates: 1862-01-18,1862-01-21 &lt;/p&gt; &lt;p&gt;Legislative action: Received in the House on January 18, 1862 and referred to the committee on public charitable institutions and received in the Senate on January 21, 1862 and concurred &lt;/p&gt; &lt;p&gt;Total signatures: 11 &lt;/p&gt; &lt;p&gt;Legislative action summary: Received, referred, received, concurred &lt;/p&gt; &lt;p&gt;Legal voter signatures (males not identified as non-legal): 11 &lt;/p&gt; &lt;p&gt;Female only signatures: No &lt;/p&gt; &lt;p&gt;Identifications of signatories: subscribers, ["others"] &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;: Manuscript &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: Massachusetts Colonization Society, colonize on the coast of Africa free people of color with their own consent, real and personal estate, [includes notes approving publication, certification of publication of petition in the Boston Daily Advertiser, Oliver Warner, secretary of state, Charles F. Dunbar, one of the proprietors of newspaper, witnessed by justice of the peace, and notice on application] &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: St. 1862, c.44, passed February 28, 1862 &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/ph7ced
title: Python Libraries and the Shape of Collective Attention: Cybersecurity and the Rise of AI

description: The Python programming language is widely used across a range of fields and lines of business. How does interest in Python libraries change over time? Demand for goods like Python is difficult to measure, in part because Python is freely available as free/libre open source software (FLOSS). FLOSS is crucial to modern digital infrastructure such as the web and cloud, and numerous scientific endeavors including modeling and data mining. One part of sustaining digital infrastructure is better understanding how it is being used. In support of this, we consider two types of external events that may serve as demand triggers: internal events, in the form of announcements of security vulnerabilities, and external events, in the form of rising interest in machine learning and large language models in particular.
description: This repository contains the code and data for reproducing:

"Python Libraries and the Shape of Collective Attention: Cybersecurity and the Rise of AI"
by Kaylea Champion and Ellie Ross

Conducted for partial fulfillment of the course requirements for CS&SS 592 A, Applied Longitudinal Analysis
Instructor: Dr. Elena Erosheva
University of Washington


1. If you would like to download fresh versions of the dataset rather than use the versions we provided, you may obtain them from:

 - Combined download figures for python: http://www.lesfleursdunormal.fr
 - CVE data from NIST: https://nvd.nist.gov/
 - Metadata via a Google BigQuery: SELECT distinct name FROM `bigquery-public-data.pypi.distribution_metadata` where CONTAINS_SUBSTR(description, 'gpt') or CONTAINS_SUBSTR(descript
ion, 'machine learning') or CONTAINS_SUBSTR(description, 'artificial intelligence') or CONTAINS_SUBSTR(description, 'llm') or CONTAINS_SUBSTR(summary, 'gpt') or CONTAINS_SUBSTR(summary, 'ma
chine learning') or CONTAINS_SUBSTR(summary, 'artificial intelligence') or CONTAINS_SUBSTR(summary, 'llm') or CONTAINS_SUBSTR(keywords, 'gpt') or CONTAINS_SUBSTR(keywords, 'ai') or CONTAINS
_SUBSTR(keywords, 'machine learning') or CONTAINS_SUBSTR(keywords, 'artificial intelligence') or CONTAINS_SUBSTR(keywords, 'llm') or CONTAINS_SUBSTR(classifiers,"Topic :: Scientific/Enginee
ring :: Artificial Intelligence")}

Note that the CVE raw dataset was filtered using the command grep pypi cve.csv &gt; cve_clean.csv

2. Update the paths at the top of prepAndClean.R and models.R to match your needs. Note that some values are saved out into a file for knitr to compile into the .Rtex of the paper itself.

3. Run prepAndClean.R

4. Run models.R

5. Optionally, run diagnostics.R

Please let us know if you have questions!
DOI: 10.7910/dvn/pqhnu0
title: Data for Mathematical tumor model, zebrafish melanoma validation showing female advantage and new potential dormancy/recurrence mechanism

description: Included are MATLAB code to solve the Growth-Reduction-Metastasis (GRM) model, images of fluorescent melanoma in zebrafish, MATLAB code to analyze the data to obtain tumor size distributions and MATLAB code to find parameters that optimally fit the size distribution data.
DOI: 10.7910/dvn/40uuka
title: Replication Data for: Vote-Buying, Anti-Corruption Campaigns, and Identity in African Elections

description: This dataset allows one to recreate the analyses run for the paper "Vote-Buying, Anti-Corruption Campaigns, and Identity in African Elections" and its accompanying online appendix included here.
DOI: 10.7910/dvn/qdtbnb
title: Daily weather data for Rwamagana (Rwanda) from 2016 to 2017

description: Daily weather data owned or distributed by AfricaRice and available on &lt;a href="http://www.mlaxmel.com/weatherdata/query.php"&gt; http://www.mlaxmel.com/weatherdata/query.php &lt;/a&gt;
DOI: 10.5281/zenodo.15169369
title: Observational dataset used in "Impact of horizontal resolution on numerical weather predicted sensible heat fluxes in London"

description: These data are used in Chapter 4, “Impact of horizontal resolution on numerical weather predicted sensible heat fluxes in London” in the University of Reading thesis "Urban surface-atmosphere exchanges: scintillometry observations and NWP evaluation" submitted by Beth Saunders, 2024. The observation dataset consists of a one-day case study (13th May 2016, day-of-year 134). The experimental setup is also described in Saunders et. al 2024: “Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London”, DOI: https://doi.org/10.1002/qj.4837. File names and subdirectories are named in the format year + day of year (DOY).

AWS processed:

Processed and quality-control checked data from a Davis Vantage Pro Plus located in the city of London (Saunders et al. 2024).

CNR4 processed:

Processed and quality-control checked data from a Kipp & Zonen CNR4 radiometer in the city of London (Saunders et al. 2024).

LAS raw:

Raw data from a single beam Kipp and Zonen MKII LAS (850 nm wavelength) sensor defined as the scintillometer path ‘BCT-IMU’ (Saunders et al. 2024) located between the City of London and Islington.

LAS source areas:

Source areas for the scintillometer path ‘BCT-IMU’. Data are derived using the methodology as described by Saunders et al. 2024 (see Fig. 2), and were written using the ‘scintools’ python code (10.5281/zenodo.7434074). Digital surface models used for these cases are also included as part of the ‘scintools’ zenodo repository.

Source areas for the specific case study day are processed using the python code ‘scint_fp’ (included in 10.5281/zenodo.7434153) using automatic weather station data included here (in the directory AWS processed). csv files included in each subdirectory of ‘LAS_Source_Areas’ are the inputs given to ‘scint_fp’.

Source areas are calculated every hour using inputs averaged over the last 10 minutes of that our (time-ending). For example, a 1200 source area is calculated using meteorological conditions averaged over 1150-1200. The end of each file name indicates the time period (time ending) of which the source area applies.

LAS processed:

Processed data from the scintillometer path ‘BCT-IMU’. Data were derived using the methodology as described by Saunders et al. 2024 (see Fig. 3), and written using the ‘scint_flux’ python code (10.5281/zenodo.7434143) and using scintillometer source areas derived from the ‘scintools’ python code (10.5281/zenodo.7434074).

Files ending with PERIOD_VAR_## refer to the averaging period performed on the LAS raw data as number in minutes (where ## is 1, 10, and 60 minutes). Files with a name including ‘sa10min_ending’ use source areas calculated every hour with input meteorological conditions averaged over 10 minutes.
description: The authors acknowledge the funding and support from the NERC Case studentship (NE/N008103/1) and ERC urbisphere (855005).
DOI: 10.5281/zenodo.15169370
title: Observational dataset used in "Impact of horizontal resolution on numerical weather predicted sensible heat fluxes in London"

description: These data are used in Chapter 4, “Impact of horizontal resolution on numerical weather predicted sensible heat fluxes in London” in the University of Reading thesis "Urban surface-atmosphere exchanges: scintillometry observations and NWP evaluation" submitted by Beth Saunders, 2024. The observation dataset consists of a one-day case study (13th May 2016, day-of-year 134). The experimental setup is also described in Saunders et. al 2024: “Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London”, DOI: https://doi.org/10.1002/qj.4837. File names and subdirectories are named in the format year + day of year (DOY).

AWS processed:

Processed and quality-control checked data from a Davis Vantage Pro Plus located in the city of London (Saunders et al. 2024).

CNR4 processed:

Processed and quality-control checked data from a Kipp & Zonen CNR4 radiometer in the city of London (Saunders et al. 2024).

LAS raw:

Raw data from a single beam Kipp and Zonen MKII LAS (850 nm wavelength) sensor defined as the scintillometer path ‘BCT-IMU’ (Saunders et al. 2024) located between the City of London and Islington.

LAS source areas:

Source areas for the scintillometer path ‘BCT-IMU’. Data are derived using the methodology as described by Saunders et al. 2024 (see Fig. 2), and were written using the ‘scintools’ python code (10.5281/zenodo.7434074). Digital surface models used for these cases are also included as part of the ‘scintools’ zenodo repository.

Source areas for the specific case study day are processed using the python code ‘scint_fp’ (included in 10.5281/zenodo.7434153) using automatic weather station data included here (in the directory AWS processed). csv files included in each subdirectory of ‘LAS_Source_Areas’ are the inputs given to ‘scint_fp’.

Source areas are calculated every hour using inputs averaged over the last 10 minutes of that our (time-ending). For example, a 1200 source area is calculated using meteorological conditions averaged over 1150-1200. The end of each file name indicates the time period (time ending) of which the source area applies.

LAS processed:

Processed data from the scintillometer path ‘BCT-IMU’. Data were derived using the methodology as described by Saunders et al. 2024 (see Fig. 3), and written using the ‘scint_flux’ python code (10.5281/zenodo.7434143) and using scintillometer source areas derived from the ‘scintools’ python code (10.5281/zenodo.7434074).

Files ending with PERIOD_VAR_## refer to the averaging period performed on the LAS raw data as number in minutes (where ## is 1, 10, and 60 minutes). Files with a name including ‘sa10min_ending’ use source areas calculated every hour with input meteorological conditions averaged over 10 minutes.
description: The authors acknowledge the funding and support from the NERC Case studentship (NE/N008103/1) and ERC urbisphere (855005).
DOI: 10.7910/dvn/lekakb
title: The Field (California) Poll 98-04
DOI: 10.5281/zenodo.7434186
title: NWP model dataset used in "Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London"

description: These data are used in Saunders et. al 2024: “Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London”, DOI: https://doi.org/10.1002/qj.4837

NWP datasets (Met Office UKV model) from two days (2nd May 2016, day of year (DOY) 123, and 5th May 2016, DOY 126) used in Saunders et al. 2024. Day cases are separated by folder named in the format year + DOY.

The UKV data for the IOP days are from the operational runs, initialised at 21:00 UTC, with model output analysed from 00:00 UTC the following day for the period when observations are available. The operational UKV’s diagnostics output is on the hour.

Filenames are presented in the following example format:

MOUKV_FC2016050121Z_m01s00i002_LON_BCT.nc

This example would be from the UKV model (‘MOUKV’), from a run initialising at 2100 UTC on the 1st May 2016 (FC2016050121Z) for the variable ‘U component of wind’ (identified by the stash code ‘m01s00i002’), with 3 by 3 grid boxes overlaying the ‘BCT’ site as described by Saunders et al. 2024 (‘LON_BCT’).

The following stash codes, used by Saunders et al. 2024, are available:

m01s00i002: U component of wind

m01s00i003: V component of wind

m01s00i010: Specific humidity

m01s00i024: Surface temperature

m01s00i025: Boundary layer depth

m01s00i090: Total aerosol

m01s00i150: W component of wind

m01s00i266: Bulk clod fraction

m01s00i408: Pressure at theta levels

m01s00i409: Surface pressure

m01s01i235: Total downward surface shortwave flux

m01s02i201: Outgoing shortwave radiative flux (top of atmosphere)

m01s02i207: Incoming shortwave radiative flux (top of atmosphere)

m01s03i026: Roughness length

m01s03i202: Heat flux from surface to deep soil level 1

m01s03i216: Boundary layer heat fluxes

m01s03i217: Surface sensible heat flux

m01s03i219: X component of surface and boundary layer wind stress

m01s03i220: Y component of surface and boundary layer wind stress

m01s03i222: Boundary layer total moisture fluxes

m01s03i225: 10-meter wind U component

m01s03i226: 10-meter wind V component

m01s03i234: Surface latent heat flux

m01s03i236: Temperature at 1.5 m

m01s03i245: Relative humidity at 1.5 m

m01s03i281: Visibility t 1.5 m (including precipitation)

m01s03i290: Surface sensible heat flux on tiles

m01s03i316: Surface temperature on tiles

m01s03i321: Canopy water on tiles

m01s03i476: Combined boundary layer type

m01s04i203: Large scale rainfall rate

m01s08i225: Deep soil temperature after hydrology

m01s09i203: Low cloud amount

m01s09i204: Medium cloud amount

m01s09i205: High cloud amount

m01s09i217: Total cloud amount

m01s16i004: Temperature on theta levels

m01s16i222: Pressure at mean sea level
description: The authors acknowledge the funding and support from the NERC Case studentship (NE/N008103/1) and ERC urbisphere (855005).
DOI: 10.5281/zenodo.7434187
title: NWP model dataset used in "Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London"

description: These data are used in Saunders et. al 2024: “Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London”, DOI: https://doi.org/10.1002/qj.4837

NWP datasets (Met Office UKV model) from two days (2nd May 2016, day of year (DOY) 123, and 5th May 2016, DOY 126) used in Saunders et al. 2024. Day cases are separated by folder named in the format year + DOY.

The UKV data for the IOP days are from the operational runs, initialised at 21:00 UTC, with model output analysed from 00:00 UTC the following day for the period when observations are available. The operational UKV’s diagnostics output is on the hour.

Filenames are presented in the following example format:

MOUKV_FC2016050121Z_m01s00i002_LON_BCT.nc

This example would be from the UKV model (‘MOUKV’), from a run initialising at 2100 UTC on the 1st May 2016 (FC2016050121Z) for the variable ‘U component of wind’ (identified by the stash code ‘m01s00i002’), with 3 by 3 grid boxes overlaying the ‘BCT’ site as described by Saunders et al. 2024 (‘LON_BCT’).

The following stash codes, used by Saunders et al. 2024, are available:

m01s00i002: U component of wind

m01s00i003: V component of wind

m01s00i010: Specific humidity

m01s00i024: Surface temperature

m01s00i025: Boundary layer depth

m01s00i090: Total aerosol

m01s00i150: W component of wind

m01s00i266: Bulk clod fraction

m01s00i408: Pressure at theta levels

m01s00i409: Surface pressure

m01s01i235: Total downward surface shortwave flux

m01s02i201: Outgoing shortwave radiative flux (top of atmosphere)

m01s02i207: Incoming shortwave radiative flux (top of atmosphere)

m01s03i026: Roughness length

m01s03i202: Heat flux from surface to deep soil level 1

m01s03i216: Boundary layer heat fluxes

m01s03i217: Surface sensible heat flux

m01s03i219: X component of surface and boundary layer wind stress

m01s03i220: Y component of surface and boundary layer wind stress

m01s03i222: Boundary layer total moisture fluxes

m01s03i225: 10-meter wind U component

m01s03i226: 10-meter wind V component

m01s03i234: Surface latent heat flux

m01s03i236: Temperature at 1.5 m

m01s03i245: Relative humidity at 1.5 m

m01s03i281: Visibility t 1.5 m (including precipitation)

m01s03i290: Surface sensible heat flux on tiles

m01s03i316: Surface temperature on tiles

m01s03i321: Canopy water on tiles

m01s03i476: Combined boundary layer type

m01s04i203: Large scale rainfall rate

m01s08i225: Deep soil temperature after hydrology

m01s09i203: Low cloud amount

m01s09i204: Medium cloud amount

m01s09i205: High cloud amount

m01s09i217: Total cloud amount

m01s16i004: Temperature on theta levels

m01s16i222: Pressure at mean sea level
description: The authors acknowledge the funding and support from the NERC Case studentship (NE/N008103/1) and ERC urbisphere (855005).
DOI: 10.7910/dvn/pegiub
title: Replication Data for: Learning from Null Effects: A Bottom-Up Approach

description: Replication archive for the studies presented in "Learning from Null Effects: A Bottom-Up Approach"
description: Volume #, Issue #
DOI: 10.7910/dvn/pi8kbm
title: Replication Data for "Gendered Opportunities and Constraints"

description: Replication data and code for "Gendered Opportunities and Constraints: How Executive Sex and Approval Influence Executive Decree Issuance"
DOI: 10.7910/dvn/apeuo9
title: Educational Access and Peace Duration in Post Conflict Countries.
DOI: 10.7910/dvn/pglrbd
title: Papua New Guinea (2008): MAP Study Evaluating the Availability of Condoms and Other Health Products in Morobe, NCD and Central, Western Highlands and East New Britian Provinces. First Round.

description: The Project MAP methodology employs the Lot Quality Assurance Sampling (LQAS) technique to draw a random sample of 19 enumeration areas referred to in this report as census units (CUs). Census units are small territorial units established during the national population census, which was conducted in the year 2000 by the National Statistics Office. In the LQAS method of sampling, only one outlet selling the product needs to be present in a census unit or hot zone for the area to be considered as covered. The 19 CUs were randomly selected from provinces in each of the four regions of PNG. Central and the National Capital District, Morobe, Western Highlands, and the East New Britain Provinces. In this study, the four provinces selected for the study are referred to as supervision areas. The LQAS assessment of coverage determines the proportion of outlets in supervision areas in which health products such as condoms, lubricants, mosquito nets, etc. are available. In addition to providing a basic measure of product coverage, the LQAS assessment also determines quality of coverage, that is the proportion of outlets in each administrative area in which the products conform to additional minimum standards. The quality standards were measured using the following indicat
ors: (1) Product is clearly visible, (2) There were no stock-outs during the 4 weeks prior to the study, (3) HIV and AIDS or condom promotional items are visibly displayed in the outlet, and (4) in hot zones, condoms are available in outlets that stay open late (closing at or after 10pm). At least three of the indicators had to be met in order to meet the minimum standard of the quality of coverage. All outlets in the selected CUs were audited, and additional information was collected on the frequency of stock outs, and willingness to stock and sell the products for outlets which never stocked the product. High-risk geographic areas for HIV transmission (referred to as Âhot zones in this report) are defined by clustering hotspots with contiguous catchments. Hotspots include bars, roadside markets, guest houses, hotels, and lodges. Data was collected in March, 2008. The outlet audits were conducted with electronic survey forms on PDAs (Personal Digital Assistants), and the coordinates of each venue were recorded with GPS (Global Positioning System) units, since these are required to analyze access and to visually represent outlets on maps.
DOI: 10.15468/dl.bpduvp
title: Occurrence Download

description: A dataset containing 1988 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Ptilinopus roseicapilla (R.Lesson, 1831))",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 1988 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015130-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/ar84rf
title: Wright Casebook Edited; Docketed Case: 23,324