cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr23625.v1
title: Uniform Crime Reporting Program Data [United States]: Hate Crime Data, 2002 [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.
DOI: 10.6084/m9.figshare.17048549.v2
title: Data for: Towards the development of standardized bioassays for corals: Acute toxicity of the UV filter benzophenone-3 to scleractinian 3 coral larvae

description: File 1: Raw data table consiting acute coral larvae bioassay data after oxybenzone exposure, as well as data for recruits. <br> File 2: Modified R code for estimating LC/EC50
DOI: 10.6084/m9.figshare.19635288
title: Interaction of spatial and semantic representation of knowledge

description: The dataset contains the necessary JSON files to implement the map-enabled interaction between spatial and semantic visualizations
DOI: 10.5285/dd2a4f57-5943-68c7-e053-6c86abc0eb55
title: Slocum glider data from Units 345 ("Cabot"), 490 ("Fortyniner"), 397 ("Nelson") and 399 ("Rayleigh") in the Celtic Sea during the Shelf Sea Biogeochemistry Project, 2014-2015.

description: The Shelf Sea Biogeochemistry Project (SSB) was a 5-year project (2014 to 2019) that aimed to deliver an improved understanding of key physical-biogeochemical interactions over a range of shelf sea environments and across successive seasons. From November 2014 to August 2015, four autonomous underwater gliders (Units 345, 419, 397 and 399) were deployed in the Celtic Sea to do repeat transects from the inner shelf (Central Celtic Sea/CANDYFLOSS station) to the shelf break (Celtic Sea 2 site near the shelf break). Each glider carried a CTD packet to measure temperature and conductivity, an Aanderra oxygen optode, and a WetLabs Triplet Eco puck to measure CDOM, chlorophyll and water-column backscatter. In total, the gliders collected over 17998 profiles over a combined distance of 5941 km. The gliders captured the seasonal transition from well-mixed to stratified water column (Unit 345), including the onset of seasonal stratification (Unit 419), the spring phytoplankton bloom (Unit 399), and the highly stratified summer conditions with a pronounced sub-surface chlorophyll maximum (Unit 399). Across the 9-month deployment, and in conjunction with the SSB project, these gliders provided key insights into the physical and biogeochemical variability of the Celtic Sea region across seasonal timescales.
DOI: 10.34740/kaggle/dsv/3508088
title: Arabic Sign Language Dataset 2022
DOI: 10.18462/iif.nitec47.04.2022
title: Passive cooling technologies. 47th Informatory Note on Refrigeration Technologies.
DOI: 10.3886/icpsr23781.v1
title: Uniform Crime Reporting Program Data [United States]: Hate Crime Data, 2001 [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.
DOI: 10.5281/zenodo.6477762
title: Pig rooting surveys

description: <b>Description: </b>This is a dataset on pig rooting observations in the SAFE landscape in 2021. In 2021, the Bearded Pigs of Sabah were severely affected and decimated by the African Swine Fever. These rooting surveys were conducted to provide an indication whether there were recent Bearded pig activity in the SAFE landscape. <b>Project: </b>This dataset was collected as part of the following SAFE research project: <b>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</b><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available here<b>Files: </b>This consists of 1 file: SAFE_pig_rooting_surveys_2021_for_upload.xlsx<b>SAFE_pig_rooting_surveys_2021_for_upload.xlsx</b>This file contains dataset metadata and 1 data tables:<b>Pig rooting observations</b> (described in worksheet Data)Description: The observations on the presence or absence of Bearded pig rooting.Number of fields: 11Number of data rows: 88Fields: <b>Date</b>: Calender date of field survey (Field type: date)<b>Location</b>: Unique ID (Field type: location)<b>Observers</b>: names of the observers (Field type: id)<b>Distance_along_line_transect</b>: Distance along line transect (Field type: numeric)<b>Left_or_right</b>: observations about 10m along the left or the right of the transect line (Field type: categorical)<b>Rooting_observed</b>: soil disturbance / rooting observed (Field type: categorical)<b>Age_of_rooting</b>: rooting age (fresh = within weeks, old = 1-3 months, aged &gt;3months) (Field type: categorical)<b>Pig_tracks</b>: Presence of pig tracks (Field type: categorical trait)<b>Photo_ID</b>: File name (Field type: id)<b>Distance_from_transect</b>: Distance left or right from the transect (Field type: numeric)<b>Comments</b>: Any additional relevant information (Field type: comments)<b>Date range: </b>2021-01-01 to 2021-07-14<b>Latitudinal extent: </b>4.6680 to 4.6920<b>Longitudinal extent: </b>117.5793 to 117.5935<b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets. - Animalia <br> - - Chordata <br> - - - Mammalia <br> - - - - Artiodactyla <br> - - - - - Suidae <br> - - - - - - <i>Sus</i> <br> - - - - - - - <i>Sus barbatus</i> <br>
DOI: 10.5281/zenodo.6477761
title: Pig rooting surveys

description: <b>Description: </b>This is a dataset on pig rooting observations in the SAFE landscape in 2021. In 2021, the Bearded Pigs of Sabah were severely affected and decimated by the African Swine Fever. These rooting surveys were conducted to provide an indication whether there were recent Bearded pig activity in the SAFE landscape. <b>Project: </b>This dataset was collected as part of the following SAFE research project: <b>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</b><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available here<b>Files: </b>This consists of 1 file: SAFE_pig_rooting_surveys_2021_for_upload.xlsx<b>SAFE_pig_rooting_surveys_2021_for_upload.xlsx</b>This file contains dataset metadata and 1 data tables:<b>Pig rooting observations</b> (described in worksheet Data)Description: The observations on the presence or absence of Bearded pig rooting.Number of fields: 11Number of data rows: 88Fields: <b>Date</b>: Calender date of field survey (Field type: date)<b>Location</b>: Unique ID (Field type: location)<b>Observers</b>: names of the observers (Field type: id)<b>Distance_along_line_transect</b>: Distance along line transect (Field type: numeric)<b>Left_or_right</b>: observations about 10m along the left or the right of the transect line (Field type: categorical)<b>Rooting_observed</b>: soil disturbance / rooting observed (Field type: categorical)<b>Age_of_rooting</b>: rooting age (fresh = within weeks, old = 1-3 months, aged &gt;3months) (Field type: categorical)<b>Pig_tracks</b>: Presence of pig tracks (Field type: categorical trait)<b>Photo_ID</b>: File name (Field type: id)<b>Distance_from_transect</b>: Distance left or right from the transect (Field type: numeric)<b>Comments</b>: Any additional relevant information (Field type: comments)<b>Date range: </b>2021-01-01 to 2021-07-14<b>Latitudinal extent: </b>4.6680 to 4.6920<b>Longitudinal extent: </b>117.5793 to 117.5935<b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets. - Animalia <br> - - Chordata <br> - - - Mammalia <br> - - - - Artiodactyla <br> - - - - - Suidae <br> - - - - - - <i>Sus</i> <br> - - - - - - - <i>Sus barbatus</i> <br>
DOI: 10.5281/zenodo.6477748
title: DNA reference database for macroinvertebrates

description: This repository hosts a ready-to-use database for the study of macroinvertebrate diversity using DNA metabarcoding with the fwhF2/EPTDr2n primer set (Leese et al. 2022). The database was developed from the MIDORI database and completed with some data from the BOLD database. The process of preparation and curation of the database is described below: 1. The MIDORI CO1 RAW v247 database was downloaded from the official servers. 2. Reference sequences were matched against the fwhF2 forward primer sequences. Non-matching sequences were removed. Bases preceding the forward primer were removed. 3. Reference sequences with a length of less than 142 bp were removed. Bases following the position 142 were removed. 4. Taxonomy was simplified to keep only 8 ranks: superkingdom, kingdom, phylum, class, order, family, genus and species. 5. Taxonomic nomenclature was harmonized using `refdb::refdb_clean_tax_harmonize_nomenclature` 6. Extra words were removed from taxonomic names using `refdb::refdb_clean_tax_remove_extra` 7. Subspecific information were removed from taxonomic names using `refdb::refdb_clean_tax_remove_subsp` 8. Missing taxonomic names were identified and normalized using `refdb::refdb_clean_tax_NA`. 9. Hybrids and taxonomic names with qualifiers of uncertainty were converted to NA. 10. Duplicates (identical sequences and taxonomy) were removed. 11. Sequences with more than ambiguous nucleotides (N) were removed). 12. Sequences with low taxonomic precision (above phylum) were removed. 13. A random subset of 10 sequences was retained for each taxa. 14. For a given taxonomic rank, records with NA values were removed if they were not the only representative of the upper clade. 15. For Switzerland: Missing EPT species and IBCH families were searched in BOLD and merged with the reference database. The same filters and cleaning steps (2-14) were then applied.
DOI: 10.5281/zenodo.6477747
title: DNA reference database for macroinvertebrates

description: This repository hosts a ready-to-use database for the study of macroinvertebrate diversity using DNA metabarcoding with the fwhF2/EPTDr2n primer set (Leese et al. 2022). The database was developed from the MIDORI database and completed with some data from the BOLD database. The process of preparation and curation of the database is described below: 1. The MIDORI CO1 RAW v247 database was downloaded from the official servers. 2. Reference sequences were matched against the fwhF2 forward primer sequences. Non-matching sequences were removed. Bases preceding the forward primer were removed. 3. Reference sequences with a length of less than 142 bp were removed. Bases following the position 142 were removed. 4. Taxonomy was simplified to keep only 8 ranks: superkingdom, kingdom, phylum, class, order, family, genus and species. 5. Taxonomic nomenclature was harmonized using `refdb::refdb_clean_tax_harmonize_nomenclature` 6. Extra words were removed from taxonomic names using `refdb::refdb_clean_tax_remove_extra` 7. Subspecific information were removed from taxonomic names using `refdb::refdb_clean_tax_remove_subsp` 8. Missing taxonomic names were identified and normalized using `refdb::refdb_clean_tax_NA`. 9. Hybrids and taxonomic names with qualifiers of uncertainty were converted to NA. 10. Duplicates (identical sequences and taxonomy) were removed. 11. Sequences with more than ambiguous nucleotides (N) were removed). 12. Sequences with low taxonomic precision (above phylum) were removed. 13. A random subset of 10 sequences was retained for each taxa. 14. For a given taxonomic rank, records with NA values were removed if they were not the only representative of the upper clade. 15. For Switzerland: Missing EPT species and IBCH families were searched in BOLD and merged with the reference database. The same filters and cleaning steps (2-14) were then applied.
DOI: 10.6084/m9.figshare.19579363
title: Dataset 2.xlsx

description: List of <em>P</em>. <em>aeruginosa</em> genes required for desiccation tolerance from experiment 2 following 1 or 3 days when desiccated in the presence (LB broth) or absence (PBS) of nutrients on plastic surfaces.
DOI: 10.6084/m9.figshare.19579363.v1
title: Dataset 2.xlsx

description: List of <em>P</em>. <em>aeruginosa</em> genes required for desiccation tolerance from experiment 2 following 1 or 3 days when desiccated in the presence (LB broth) or absence (PBS) of nutrients on plastic surfaces.
DOI: 10.6084/m9.figshare.19579366
title: Dataset 1.xlsx

description: List of <em>P</em>. <em>aeruginosa</em> genes required for desiccation tolerance following 1, 3, or 7 days of desiccation on plastic and stainless steel surfaces identified in the first Tn-seq experiment.
DOI: 10.6084/m9.figshare.19579369
title: Dataset 3.xlsx

description: List of 111 <em>P</em>. <em>aeruginosa</em> genes identified in all desiccated conditions and gene clusters.
DOI: 10.6084/m9.figshare.19579369.v1
title: Dataset 3.xlsx

description: List of 111 <em>P</em>. <em>aeruginosa</em> genes identified in all desiccated conditions and gene clusters.
DOI: 10.6084/m9.figshare.19579366.v1
title: Dataset 1.xlsx

description: List of <em>P</em>. <em>aeruginosa</em> genes required for desiccation tolerance following 1, 3, or 7 days of desiccation on plastic and stainless steel surfaces identified in the first Tn-seq experiment.
DOI: 10.6084/m9.figshare.19585921.v1
title: Supplement B

description: Tn-seq sequencing library preparation
DOI: 10.3886/icpsr23783.v1
title: Uniform Crime Reporting Program Data [United States]: Hate Crime Data, 2000 [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.
DOI: 10.25345/c5j38kn1k
title: MassIVE MSV000089298 - Proteomic analysis of the peptic and pancreatic digests of infant formula ingredients
title: Whey protein and serum protein concentrate (WPC, SPC) are infant formula (IF) ingredients. SPC was produced under different processing conditions. SPC; heat-treated (80 degree, 30 s) SPC (HT-SPC); heat-treated and stored SPC (six weeks at 37 degree, HTS-SPC) were compared to a whey protein concentrate (WPC), as a control. Whey protein-based IF ingredients (SPC, HT-SPC, HTS-SPC, WPC) were dissolved in Milli-Q water (Millipore Corp., MA, USA). Protein (0.5 g) was dissolved in 33.3 mL Milli-Q water to a final protein concentration of 15 mg/mL. Samples were subjected to in vitro digestion using pepsin followed by pancreatin to simulate infant gastric and intestinal digestion. For pepsin digestion, the pH was adjusted to 4 by 1 M HCl and pepsin (Sigma P7000) was added at a protein/enzyme of 1.25% (w/w). After pepsin digestion for 2 h, 1% of Pepstatin A (1 mg/mL, Sigma 77170) was added to terminate the reaction. The pH was then adjusted to 6.5 by 1.43 M sodium bicarbonate (Fluka, Roskilde, Denmark) and pancreatin (Sigma P7545) was added at a protein/substrate ratio of 1.5% (w/w). After pancreatic digestion for 6 h, 100 micro liter of cOmplete Protease inhibitor cocktail (1 tablet in 25 mL of Milli-Q water, Roche, Basel, Switzerland) was added to terminate the reaction.