cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr36397
title: Uniform Crime Reporting Program Data: Hate Crime Data, 2014 [Record-Type Files]
title: Archival Version

description: In response to a growing concern about hate crimes, the
United States Congress enacted the Hate Crime Statistics Act of
1990. The Act requires the attorney general to establish guidelines
and collect, as part of the Uniform Crime Reporting (UCR) Program,
data "about crimes that manifest evidence of prejudice based on race,
religion, sexual orientation, or ethnicity, including where
appropriate the crimes of murder and non-negligent manslaughter,
forcible rape, aggravated assault, simple assault, intimidation,
arson, and destruction, damage or vandalism of property." Hate crime
data collection was required by the Act to begin in calendar year 1990
and to continue for four successive years. In September 1994, the
Violent Crime Control and Law Enforcement Act amended the Hate Crime
Statistics Act to add disabilities, both physical and mental, as
factors that could be considered a basis for hate crimes. Although the
Act originally mandated data collection for five years, the Church
Arson Prevention Act of 1996 amended the collection duration "for each
calendar year," making hate crime statistics a permanent addition to
the UCR program. As with the other UCR data, law enforcement agencies
contribute reports either directly or through their state reporting
programs. Information contained in the data includes number of victims
and offenders involved in each hate crime incident, type of victims,
bias motivation, offense type, and location type.
description: Datasets: 
DS0: Study-Level Files
DS1: Batch Header File
DS2: Incident Record File
description: Hate crime incidents reported by law enforcement agencies
in the United States. Smallest Geographic Unit: state, county, jurisdiction
description: computer-assisted self interview (CASI); self-enumerated questionnaireThe original hierarchical file has been split into two files: a Batch Header File and an Incident Record File. The Batch Header File contains a record for each law enforcement agency whether or not they reported an incident. The Batch Header File variables are merged into the Incident Record File for those agencies that reported an incident.Each victim-type variable has been split into eight separate flag variables to record each victim type. Previously, the variables were eight-character strings that contained the first letter of each victim type in any order.At the recommendation of the CJIS APB and with the approval of the FBI Director, the FBI UCR Program initiated the collection of rape data under a revised definition and removed the term "forcible" from the offense name in 2013. The changes bring uniformity to the offense in both the Summary Reporting System (SRS) and the NIBRS by capturing data (1) without regard to gender, (2) including penetration of any bodily orifice by any object or body part, and (3) including offenses where physical force is not involved. As a result of this decision, the program renamed the SRS and NIBRS sex offenses - Forcible Rape to Rape, Forcible Sodomy to Sodomy, and Forcible Fondling to Fondling.
DOI: 10.3535/nsb-8sr-0hm
title: Agrostis stolonifera L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/z0a-60g-q6w
title: Agrostis stolonifera L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3886/icpsr36394
title: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, United States, 2014
title: Archival Version

description: These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each month by police agencies in the United States. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform Crime Report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses including violent, drug, gambling, and larceny crimes. The data received by ICPSR were structured as a hierarchical file containing (per reporting police agency) an agency header record, and 1 to 12 monthly header reports, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to a rectangular format.
description: The data for the Uniform Crime Reports are submitted voluntarily by city, county, and state law enforcement agencies. Some agencies receive forms directly from the FBI and return them directly to the FBI. Many others receive forms from state collecting programs and return the reports to those programs. The state programs, in turn, forward the reports to the FBI. Reports are sent out and collected on a monthly basis.
description: Datasets: 
DS1: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, United States, 2014
description: Arrests reported by law enforcement agencies to the Federal Bureau of Investigation's Uniform Crime Reporting program. Smallest Geographic Unit: states, counties, cities
description: mail questionnaire; on-site questionnaire
DOI: 10.3535/52x-6s9-1pl
title: Achnatherum calamagrostis (L.) P.Beauv.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/dwl-mlg-v63
title: Cupania scrobiculata Rich.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/vm3-mqy-ype
title: Sorghum bicolor (L.) Moench

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3886/icpsr36399
title: Uniform Crime Reporting Program Data: County-Level Detailed Arrest and Offense Data, United States, 2014
title: Archival Version

description: This data collection contains county-level counts of arrests and offenses for Part I offenses (murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, and arson) and counts of arrests for Part II offenses (forgery, fraud, embezzlement, vandalism, weapons violations, sex offenses, drug and alcohol abuse violations, gambling, vagrancy, curfew violations, and runaways).
description: Datasets: DS0: Study-Level Files DS1: Arrests, All Ages DS2: Arrests, Adult DS3: Arrests, Juveniles DS4: Crimes Reported DS5: Allocated Statewide Data for Arrests, All Ages DS6: Allocated Statewide Data for Arrests, Adults DS7: Allocated Statewide Data for Arrests, Juveniles DS8: Allocated Statewide Data for Crimes Reported
description: County law enforcement agencies in the United States. Smallest Geographic Unit: county
description: self-enumerated questionnaireTwo major changes to the Uniform Crime Reports (UCR) county-level files were implemented beginning with the 1994 data. A new imputation algorithm to adjust for incomplete reporting by individual law enforcement jurisdictions was adopted. Within each county, data from agencies reporting 3 to 11 months of information were weighted to yield 12-month equivalents. Data for agencies reporting less than three months of data were replaced with data estimated by rates calculated from agencies reporting 12 months of data located in the agency's geographic stratum within its state. Secondly, a new Coverage Indicator was created to provide users with a diagnostic measure of aggregated data quality in a particular county. Data from agencies reporting only statewide figures were allocated to the counties in the state in proportion to each county's share of the state population.In the arrest files (Parts 1-3 and 5-7), data were estimated for agencies reporting zero months based on the procedures mentioned above. However, due to the structure of the data received from the Federal Bureau of Investigation (FBI), estimations could not be produced for agencies reporting zero months in the crimes reported files (Parts 4 and 8). Offense data for agencies reporting one or two months are estimated using the above procedures. Users are encouraged to refer to the codebook for more information.No arrest data were provided for Florida and Illinois. Limited crimes reported data were available for Alaska, Mississippi, and South Dakota.UCR program staff at the Federal Bureau of Investigation (FBI) were consulted in developing the new adjustment procedures. However, these UCR county-level files are not official FBI UCR releases and are being provided for research purposes only. Users with questions regarding these UCR county-level data files can contact the National Archive of Criminal Justice Data at ICPSR.
DOI: 10.3535/27e-vmh-e6s
title: Felicia aethiopica (Burm. fil.) Grau

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/e5q-9gr-y77
title: Sorghum bicolor subsp. verticilliflorum (Steud.) de Wet ex Wiersema & J.Dahlb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2a7-e58-w2r
title: Oxalis grisea A. St.-Hil. & Naudin

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/xc4-2xx-vp3
title: Erica haemastoma J. C. Wendl.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ld7-q8m-gma
title: Achnatherum calamagrostis (L.) P.Beauv.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/rfw-4zd-j4d
title: Agrostis alpina Scop.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/c13-n48-0yt
title: Sporobolus piliferus (Trin.) Kunth

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/dkz-vj9-204
title: Oxalis reclinata Jacq.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/1n9-04k-nxa
title: Sporobolus vaginiflorus (Torr. ex A.Gray) Alph.Wood

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3c7-8r5-0jl
title: Felicia hispida (DC.) Grau

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/5pk-tk4-str
title: Agrostis alpina Scop.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/t7c-ce8-s8j
title: Sporobolus virginicus (L.) Kunth

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/f5k-7a6-mnh
title: Muhlenbergia repens (J.Presl) Hitchc.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/fzw-93j-mkn
title: Oxalis hedysarifolia Raddi

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ewg-1mb-leb
title: Oxalis goniorhiza Eckl. & Zeyh.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/frg-x5a-j6s
title: Oxalis hedysarifolia Raddi

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/fs0-9qb-mlv
title: Sorghum halepense (L.) Pers.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/j6z-dvt-fzs
title: Sorghum bicolor (L.) Moench

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/s2v-hml-qef
title: Stephanotis rubicunda (K. Schum.) S. Reuss, Liede & Meve

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ed0-ytq-mns
title: Achnatherum calamagrostis (L.) P.Beauv.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/l9v-73p-dq3
title: Strophanthus gracilis K. Schum. & Pax

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.k9jqv7
title: Occurrence Download

description: A dataset containing 70449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycopodium annotinum L.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 70449 records from 319 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007096-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1374252.10fu
title: SH1374252.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374254.10fu
title: SH1374254.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374256.10fu
title: SH1374256.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374257.10fu
title: SH1374257.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374258.10fu
title: SH1374258.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374259.10fu
title: SH1374259.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374263.10fu
title: SH1374263.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374264.10fu
title: SH1374264.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374265.10fu
title: SH1374265.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1374268.10fu
title: SH1374268.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.