cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr36989
title: Examining Prosecutorial Discretion in Federal Criminal Cases, [United States], 2002-2010
title: Archival Version

description: These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.
This study directly examined the nature and characteristics of cases prosecuted in the federal courts by analyzing prosecutorial decisions to proceed with charges (or not) once an arrest is initiated, and to investigate any adjustment from the arresting offense to the charging offense. These decisions were analyzed to document their correlates and identify variation across case type.
The collection contains 1 SPSS data file (2002-10-Arrest-cases--FINAL-ANALYSIS.sav (n=794,807; 43 variables)) and 1 SPSS syntax file.
description: The primary focus of the study was to assess prosecutorial discretion in federal court cases, specifically:
1. What factors influence a prosecutor's decision to proceed with charges against an individual arrested for a federal offense?
2. Given the prosecutor's decision to proceed with charges, is the charge(s) similar or different from the original arresting charge?
3. Does case type impact a prosecutor's decision to proceed with charges and/or explain any differences between the arresting charge and the final charge?
4. Do prosecutorial decisions to proceed with charges and/or select a final charge vary by district?
description: Data for this project were drawn from the Federal Justice Statistics Program database housed within the National Archive of Criminal Justice Data (NACJD) for the years 2000-2010. Datasets were merged by arrest record. Arrests involving more than three charges were removed from further analysis (N=271).
description: The data include 43 variables and 794,807 cases. Variables include whether or not a case was dismissed by the prosecutor, and whether or not the offense charged by the prosecutor was different from the arresting charge, or in other words, whether the charge was changed by the prosecutor. Also included are temporal or geographic case characteristics: which of the 94 federal districts, the arresting agency, whether the case was forwarded to a U.S. Magistrate or filed in U.S. District Court, whether the case was considered a priority, and a measure of district case load. Demographic variables include arrestee's sex, race, age, citizenship status, and whether they were born outside of the US.
description: Presence of Common Scales: 
					none
description: Response Rates: 
					N/A
description: Datasets: 
					
						DS1: Dataset
description: record abstracts
description: Individuals arrested for a federal offense by any federal agency, 2002-2010
				   		Smallest Geographic Unit: Federal judicial district
description: Data are not a sample.
DOI: 10.5281/zenodo.14837265
title: National Checklists: Honduras Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13317251
title: National Checklists: Honduras Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13317252
title: National Checklists 2019: Honduras Species List

description: Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.
A list of species from Honduras collected using effechecka and geonames polygons
DOI: 10.3886/icpsr36835.v1
title: Uniform Crime Reporting Program Data: Hate Crime Data, 2015 [Record-Type Files]
title: Version 1

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: 10.5281/zenodo.13316400
title: National Checklists: Austria Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316401
title: National Checklists 2019: Austria Species List

description: Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.
A list of species from Austria collected using effechecka and geonames polygons
DOI: 10.5281/zenodo.14836413
title: National Checklists: Austria Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316497
title: National Checklists: Cyprus Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14836744
title: National Checklists: Cyprus Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14839969
title: unimpeded: lcdm planck_2018_plik+sn.pantheon

description: cosmological model:lcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.5281/zenodo.14839970
title: unimpeded: lcdm planck_2018_plik+sn.pantheon

description: cosmological model:lcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.5281/zenodo.13317243
title: National Checklists: France Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14836865
title: National Checklists: France Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.15468/dl.zftgkv
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Miconia oinochrophylla Donn.Sm."
 ]
}

 The dataset includes 27 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022843-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13316386
title: National Checklists: Argentina Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316387
title: National Checklists 2019: Argentina Species List

description: Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.
A list of species from Argentina collected using effechecka and geonames polygons
DOI: 10.5281/zenodo.14836404
title: National Checklists: Argentina Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14836593
title: National Checklists: Belarus Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13317310
title: National Checklists: Belarus Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316417
title: National Checklists 2019: Benin Species List

description: Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.
A list of species from Benin collected using effechecka and geonames polygons
DOI: 10.5281/zenodo.14836558
title: National Checklists: Benin Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316416
title: National Checklists: Benin Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13317429
title: National Checklists: Georgia Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14836941
title: National Checklists: Georgia Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.3886/icpsr38089
title: Firearm Injury Surveillance Study, 1993-2017
title: Archival Version

description: These data were collected using the National Electronic Injury Surveillance System (NEISS), the primary data system of the United States Consumer Product Safety Commission (CPSC). CPSC began operating NEISS in 1972 to monitor product-related injuries treated in United States hospital emergency departments (EDs). In June 1992, the National Center for Injury Prevention and Control (NCIPC), within the Centers for Disease Control and Prevention, established an interagency agreement with CPSC to begin collecting data on nonfatal firearm-related injuries in order to monitor the incidents and the characteristics of persons with nonfatal firearm-related injuries treated in United States hospital EDs over time. This dataset represents all nonfatal firearm-related injuries (i.e., injuries associated with powder-charged guns) and all nonfatal BB and pellet gun-related injuries reported through NEISS from 1993 through 2017. The cases consist of initial ED visits for treatment of the injuries. Cases were reported even if the patients subsequently died. Secondary visits and transfers from other hospitals were excluded. Information is available on injury diagnosis, firearm type, use of drugs or alcohol, criminal incident, and locale of the incident. Demographic information includes age, sex, and race of the injured person.
description: A weight variable (WT_C) is present in the data.
description: The sample design of NEISS is a stratified, probability sample of all United States hospitals that had at least six beds and provided 24-hour emergency services. There were four hospital-sized strata (defined as very large, large, medium, and small, based on the number of annual ED visits) and one children's hospital stratum. From 1993 through 1996, there were 91 NEISS hospital EDs in the sample. In 1997, the sampling frame was updated so that in 1997 through 1999 the sample included 101 NEISS hospital EDs. In 2000-2001, one NEISS hospital dropped out of the system, so there were 100 NEISS hospital EDs in the sample. In 2002, another hospital dropped out of the system, so there were 99 NEISS hospital EDs in the sampling frame. In 1997, CPSC collected firearm-related cases using the "old" and "new" NEISS hospital samples for a nine-month period. This dataset includes data from the "new" sample. The overlapping "old" sample is not included. Comparisons of weighted estimates based on the "old" and "new" samples indicated a difference of about 1 percent in the overall national estimate using these samples. The characteristics of firearm-related cases from these two overlapping samples were also very similar.
description: Datasets: DS1: Firearm Injury Surveillance Study, 1993-2017
description: United States hospitals providing emergency services.
description: computer-assisted self interview (CASI); self-enumerated questionnaire
DOI: 10.5281/zenodo.14836704
title: National Checklists: Cameroon Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316461
title: National Checklists: Cameroon Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14844229
title: Doppler lidar measurements from Villingen-Schwenningen, July 2023, Swabian MOSES 2023, Level 0 netCDF files

description: Original Doppler lidar measurements of the Doppler lidar system Lockheed Martin WindTracer WTX located at Villingen-Schwenningen, Germany (48.0563°N, 8.487°E) for the month of July 2023, sampled during the Swabian MOSES 2023 field campaign. 
description: Instrument: Lockheed Martin WindTracer WTX
DOI: 10.5281/zenodo.14844230
title: Doppler lidar measurements from Villingen-Schwenningen, July 2023, Swabian MOSES 2023, Level 0 netCDF files

description: Original Doppler lidar measurements of the Doppler lidar system Lockheed Martin WindTracer WTX located at Villingen-Schwenningen, Germany (48.0563°N, 8.487°E) for the month of July 2023, sampled during the Swabian MOSES 2023 field campaign. 
description: Instrument: Lockheed Martin WindTracer WTX
DOI: 10.5281/zenodo.14845857
title: Groundwater level modelling ensemble for Bayesian Model Averaging

description: This repository contains data files for the paper entitled:"Comparing physics-based, conceptual and machine-learning models to predict groundwater levels by BMA"

written by: Thomas Wöhling, Alvaro Oliver Crespo Delgadillo, Moritz Kraft and Anneli Guthke

submitted to the Journal Groundwater (Wiley). For further enquiries contact: thomas.woehling@tu-dresden.de

1) MODEL ENSEMBLESThe folder ENSEMBLES contains 5 Matlab-structures with model ensembles.Each ensemble consists of model realizations of 6 different models (see paper).

Each structure contains the following variables:

    *.GW_levels ... a matrix of [m x n] model realizations (simulations of groundwater levels in [m.a.s.l.]), where                m = number of realitaions and n = number of time steps    *.Model_Id ... signifies a [n,1] vector of model numbers of the ensemble members (1..6)    *.Time_vector ... time vector [1,n] in Matlab format    *.Observations ... the [1,n] vector of observed groundwater levels in [m.a.s.l.]    *.LL ... the [m,1] vector of likelihood values for each model realization    *.BMA_weights ... the [1,6] vector of BMA model weights

Note, in case of the "All_wells"- Ensemble, the observation vector is a [4,n] matrix.
DOI: 10.5281/zenodo.14845858
title: Groundwater level modelling ensemble for Bayesian Model Averaging

description: This repository contains data files for the paper entitled:"Comparing physics-based, conceptual and machine-learning models to predict groundwater levels by BMA"

written by: Thomas Wöhling, Alvaro Oliver Crespo Delgadillo, Moritz Kraft and Anneli Guthke

submitted to the Journal Groundwater (Wiley). For further enquiries contact: thomas.woehling@tu-dresden.de

1) MODEL ENSEMBLESThe folder ENSEMBLES contains 5 Matlab-structures with model ensembles.Each ensemble consists of model realizations of 6 different models (see paper).

Each structure contains the following variables:

    *.GW_levels ... a matrix of [m x n] model realizations (simulations of groundwater levels in [m.a.s.l.]), where                m = number of realitaions and n = number of time steps    *.Model_Id ... signifies a [n,1] vector of model numbers of the ensemble members (1..6)    *.Time_vector ... time vector [1,n] in Matlab format    *.Observations ... the [1,n] vector of observed groundwater levels in [m.a.s.l.]    *.LL ... the [m,1] vector of likelihood values for each model realization    *.BMA_weights ... the [1,6] vector of BMA model weights

Note, in case of the "All_wells"- Ensemble, the observation vector is a [4,n] matrix.
DOI: 10.5281/zenodo.13316856
title: National Checklists: Sudan Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14838036
title: National Checklists: Sudan Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.13316702
title: National Checklists: Malawi Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.5281/zenodo.14837700
title: National Checklists: Malawi Species List

description: Data from: GBIF.org (23 January 2025) GBIF Occurrence Download https://doi.org/10.15468/dl.vd2ajk
DOI: 10.3886/icpsr37021
title: National Corrections Reporting Program, 1991-2016: Selected Variables
title: Archival Version

description: The National Corrections Reporting Program (NCRP) compiles offender-level data on admissions and releases from state and federal prisons and post-confinement community supervision. The data are used to monitor the nation's correctional population and address specific policy questions related to recidivism, prisoner reentry, and trends in demographic characteristics of the incarcerated and community supervision populations. The Bureau of Justice Statistics (BJS) has administered the NCRP since 1983. Abt Associates has served as the NCRP data collection agent since October 2010. This version of the NCRP contains selected variables making it suitable for public release.
description: Datasets: DS0: Study-Level Files DS1: Term Records DS2: Prison Admissions DS3: Prison Releases DS4: Year-end Population
description: All persons admitted to state prison, released from state prison, or in state prison at year-end from 1991 to 2016 in the United States. Smallest Geographic Unit: State
description: mail questionnaire
DOI: 10.5255/ukda-sn-8982-1
title: Individual Survey for Evaluation of Armenia Women's Empowerment Project, 2015
title: Effectiveness Review
title: Effectiveness Review Series

description: &lt;p&gt;The &lt;span style="font-style: italic;"&gt;Individual Survey for Evaluation of Armenia Women's Empowerment Project, 2015&lt;/span&gt; data were collected by Oxfam for an Effectiveness Review of the 'Women's economic empowerment in rural communities of Vayots Dzor region' project in the 2015/16 financial year. It should be noted that this project was implemented more than two years earlier, from April 2011 to March 2013. &lt;br&gt;&lt;br&gt;The project aimed to support women to achieve greater empowerment. Activities included (1) establishing four cooperatives (three with 100 percent women and one with 50 percent women), (2) constructing four community greenhouses (500 sq. metre coverage each), (3) establishing three cold-storages for agricultural crops, (4) promoting innovative agro-technology practices on water management, (5) providing technical agricultural support during the vegetable production cycle, (6) offering training on business management, marketing, agricultural technology, the financial sector and gender, (7) linking cooperatives with a non-profit micro-finance institute providing zero-interest loans and (8) supporting cooperatives to market products by organising two harvest market days, increasing awareness of market prices via mobile SMS text to participants and supporting the cooperative with packaging and promotions. &lt;br&gt;&lt;br&gt;Data collection took place in November 2015 in Vayots Dzor region. The intervention group consisted of women that had been supported by the project (either as a participant or as the wife of a participant). The comparison group consisted of women in neighbouring communities that had similar characteristics to the intervention group in 2010, but who had not participated in the project and had never been involved in any Oxfam project.&lt;br&gt;&lt;br&gt;&lt;/p&gt;
DOI: 10.25346/s6eecgix/vy4swk
title: msXV_108v_65_48.tif
DOI: 10.25346/s6eecgix/4ax0ng
title: msXV_108v_65_47.tif
DOI: 10.25346/s6eecgix/bxczx6
title: msXV_108v_65_46.tif
DOI: 10.25346/s6eecgix/p4grba
title: msXV_108v_65_45.tif
DOI: 10.25346/s6eecgix/kbk6wn
title: msXV_108v_65_44.tif
DOI: 10.25346/s6eecgix/rxcak7
title: msXV_108v_65_43.tif
DOI: 10.25346/s6eecgix/7u2npp
title: msXV_108v_65_42.tif
DOI: 10.15468/dl.j235dj
title: Occurrence Download

description: A dataset containing 46407 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bosque Mesófilo de Montaña de México"
 ]
}

 The dataset includes 46407 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031300-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/phhxqp
title: msXV_108v_65_41.tif
DOI: 10.25346/s6eecgix/mbloyy
title: msXV_108v_65_40.tif
DOI: 10.25346/s6eecgix/b70pub
title: msXV_108v_65_39.tif
DOI: 10.25346/s6eecgix/kqrteq
title: msXV_108v_65_38.tif
DOI: 10.25346/s6eecgix/sip8ma
title: msXV_108v_65_37.tif
DOI: 10.25346/s6eecgix/khjrxk
title: msXV_108v_46_36.tif
DOI: 10.25346/s6eecgix/qxkipo
title: msXV_108v_46_35.tif
DOI: 10.25346/s6eecgix/qdtbvs
title: msXV_108v_46_34.tif
DOI: 10.25346/s6eecgix/slsil8
title: msXV_108v_46_33.tif
DOI: 10.25346/s6eecgix/4e3sac
title: msXV_108v_46_32.tif
DOI: 10.25346/s6eecgix/vbxtir
title: msXV_108v_46_31.tif
DOI: 10.25346/s6eecgix/6dwkwq
title: msXV_108v_46_30.tif
DOI: 10.25346/s6eecgix/mxqkav
title: msXV_108v_46_29.tif