Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs).Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

A multistage tandem mass spectral database using a variety of structurally defined glycans. It provides tools for glycomics research that enable users to identify glycans by spectral matching. The database stores MS2, MS3, and MS4 spectra of N-and O-linked glycans, and glycolipid glycans as well as the partial structures of these glycans.

A public repository of metabolite information as well as tandem mass spectrometry data is provided to facilitate metabolomics experiments. It contains structures and represents a data management system designed to assist in a broad array of metabolite research and metabolite identification. An annotated list of known metabolites and their mass, chemical formula, and structure are available. Each metabolite is linked to outside resources for further reference and inquiry. MS/MS data is also available on many of the metabolites.

Find Massachusetts health data by community, county, and region, including population demographics. Build custom data reports with over 100 health and social determinants of health data indicators and explore over 28,000 current and historical data layers in the map room.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

(Version 20230306, btmsp files modified May 31, 2023, additional taxonomic information added Dec 27, 2024)

Version 4.2 (20230306) of the RKI MALDI-ToF mass spectra database represents the third update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI Database v.4.2 now contains a total of 11055 MALDI-ToF mass spectra from 1601 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as Bacillus anthracis, Brucella melitensis, Yersinia pestis, Burkholderia mallei / pseudomallei and Francisella tularensis as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (zenodo db 230306.zip) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (230306-ZENODO-Metadata.pdf) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).

Version 20230306 of the RKI database contains for the first time files in the btmsp format (e.g. 2023-May-23-Bacillus-RKI-Database-568.btmsp and others). These files were generated using the MALDI Biotyper software (Bruker Daltonics) and contain a total of 1601 main spectra (msp) from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.btmsp files can be imported and used for identification with this software solution. Please refer to the manufacturer's manual for details on importing btmsp files. Note that the btmsp file available in database version 4 is broken and cannot be imported.

The pkf files (230306_ZENODO_30Peaks_0.75.pkf, 230306_ZENODO_45Peaks_0.75.pkf) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at https://wiki-ms.microbe-ms.com.

The Excel file Taxonomy information - RKI MALDI-ToF MS database of HPB at ZENODO v.4.xlsx contains additional taxonomic information such as a detailed list of bacterial MALDI-ToF mass spectra (sheet #1), overviews on the number of spectra per strain, species or bacterial genus (sheet #2), numbers of strains per species, or genus (sheet #3), etc.

The RKI mass spectrometry database is updated regularly.

The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been possible.

• Wolfgang Beyer - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany
• Guido Werner - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany
• Alejandra Bosch - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
• Michal Drevinek - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic
• Roland Grunow, Daniela Jacob, Silke Klee, Susann Dupke and Holger Scholz - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany
• Jörg Rau - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany
• Jens Jacob - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany
• Martin Mielke - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany
• Monika Ehling-Schulz - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria
• Armand Paauw - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands
• Herbert Tomaso – Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany
• Gabriel Karner - Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria
• Rainer Borriss - Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany
• Le Thi Thanh Tam - Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam
• Xuewen Gao - College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China

For a detailed description of the database see: Lasch, P., Beyer, W., Bosch, A. et al. A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. Sci Data 12, 187 (2025). https://doi.org/10.1038/s41597-025-04504-z

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for pyrrolizidine alkaloids (PAs) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (pa_database.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PAs_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations.

The data for MSnLib are divided into several Zenodo records due to size constraints.

raw positive: 10966404raw negative: 10967081mzml positive and negative: 10966280spectral libraries: 11163380

This record includes the automatically generated spectral libraries (MSnLib) within mzmine, acquired using a flow injection method on an Orbitrap ID-X instrument, for all compound libraries. There are multiple files for each compound library containing MS2 only or MSn in two data formats (.mgf or .json) for both polarities.

MS2 contains next to all MS2 spectra all pseudo MS2 spectra (a full MSn tree merged into one spectrum per compound ion). MSn contains all individual MSn stages additionally. The first number for each file highlights the library building date.

7 Compound Libraries:

Short Name: Full name, Provider (Catalog number), total compounds (not all detected during library building)

MCEBIO: Bioactive Compound Library, MedChemExpress (HY-L001), 10,315 compounds

MCESAF: 5k Scaffold Library, MedChemExpress, (HY-L902), 4998 compounds

NIHNP: NIH NPAC ACONN collection of NP, NIH/NCATS, 3988 compounds

OTAVAPEP: Alpha-helix Peptiomimetic Library, OTAVAchemicals (a-helix-Peptido), 1298 compounds

ENAMDISC: Discovery Diversity Set -10, Enamine (DDS-10), 10,240 compounds

ENAMMOL: Carboxylic Acid Fragment Library + Random, Enamine and Molport, 4378 compounds

MCEDRUG: FDA-Approved Drug Library, MedChemExpress (HY-L022), 2610 compounds

Information regarding the SPECTYPE

no SPECTYPE or SINGLE_BEST_SCAN: Best spectrum for each precursor and energy (highest TIC)

'SAME_ENERGY' = Additionally, if a spectrum was acquired multiple times for a precursor with the same energy, they are merged into one spectrum only with the same energy (max. signal height used for each fragment signal).

'ALL_ENERGIES' = merged spectrum of all used energies (in our case 3 for each precursor, using the merged (same energy) if available).

'ALL_MSN_TO_PSEUDO_MS2' = mzmine merges all MSn into one pseudo MS2.

V5 fixed USIs

A library containing spectra upwards of 200,000 chemical compounds. Spectra include metabolites, peptides, contaminants, and lipids. All spectra and chemical structures are examined by professionals.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON SEPT. 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Since the late 1950s, the USGS has maintained a long-term glacier mass-balance program at three North American glaciers. Measurements began on South Cascade Glacier, WA in 1958, expanding to Gulkana and Wolverine glaciers, AK in 1966, and later Sperry Glacier, MT in 2005. Additional measurements have been made on Lemon Creek Glacier, AK to compliment data collected by the Juneau Icefield Research Program (JIRP; Pelto and others, 2013). Direct field measurements of point glaciological data are combined with weather and geodetic data to estimate the seasonal and annual mass balance at each glacier in both a conventional and reference surface format (Cogley and others, 2011). The analysis framework (O'Neel, 2019; prior to v 3.0 van Beusekom and others, 2010) is identical at each glacier to enable cross-comparison between output time series. Vocabulary used follows Cogley and others (2011) Glossary of Glacier Mass Balance.

Abstract: This collection contains mass spectrometry data that were collected in the research data repository Chemotion from 2019-2023. The scientists who contributed to this collection are given as additional information (-> Other) to this dataset. The dataset was collected to enable the systematic re-use of the mass spectrometry data in Chemotion repository. Method: The dataset was gained as a subset of information from Chemotion repository after (1) filtering for mass spectrometry data with a precision of 0.001 Da, and (2) removal of duplicates Other: The collection was gained from contributions of the following scientists: Alexander B. Braun, André Jung, Angela Wandler, Arnaud Westeel, Chloé Liagre, Christoph Zippel, Cornelia Mattern, Daniel Knoll, Danny Wagner, Eduard Spuling, Florian Mohr, Georg Manolikakes, Hannes Kühner, Harald Kelm, Helena Šimek, Ilga Kristine Krimmelbein, Irina Protasova, Isabelle Wessely, Jana Barylko, Janina Beck, Jasmin Busch, Jérôme Klein, Jérôme Wagner, Julian Brückel, Ksenia Kutonova, Laura Holzhauer, Lisa Schmidt, Lukas Langer, Lutz-F. Tietze, Martin Nieger, Mirja Dinkel, Miro Hałaczkiewicz, Nicolai Rosenbaum, Nicolai Wippert, Nicole Jung, Niklas Krappel, Olaf Fuhr, Patrick Hodapp, Simon Oßwald, Simone Gräßle, Stefan Bräse, Susanne Moser, Sylvain Grosjean, Sylvia Vanderheiden-Schroen, Thomas Hurrle, Victor Larignon, Vikas Aggarwal, Yannick Matt, Yichuan Wang, Zhen Zhang TechnicalInfo: The provided zip folder includes 599 datasets including at least one *.jdx file each and one metadata file "msei_final" with metadata describing the 599 datasets TechnicalInfo: The metadata file "msei_final" contains the following metadata per dataset: sample_id, molfile, ontology term, analysis_id, instruments, authors, content, molfile_id

Studies and statistics from MassDEP, its Office of Research & Standards (ORS), and other organizations.

A mass spectral database that assists in identifying compunds in life sciences, matabolomics, pharmaceutical research, toxicology, forensic investigations, environemnta analysis, food control, and industry.

This data represents processed data from chemical ionization time of flight mass spectrometry instrument used to generate each figure in the manuscript. A data dictionary is included in the dataset. This dataset is associated with the following publication: Mattila, J., J. Krug, W. Roberson, R. Burnette, S. McDonald, P. Virtaranta, J. Offenberg, and W. Linak. Characterizing volatile emissions and combustion by-products from aqueous film-forming foams using online chemical ionization mass spectrometry. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 0, (2024).

Data sets are available for consumer complaints, workplace complaints and citations, debarred contractors, bid protest decisions, and pending Open Meeting Law complaints.

The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems, the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The database is now (09-28-2021) also provided in R data format (.RDS) for use with the R programming language. This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://res1dxd-o-tdoid-o-torg.vcapture.xyz/10.18434/T4H594).

The Middle East Mass Movements Database, a part of the larger Mass Movements Project, contains basic characteristics of all mass movements in the region for each year that they mobilize at least 1,000 participants in costly action for a least a month in pursuit of a common political goal. The data are the result of a lengthy coding process in which two researchers independently explore each known mobilization with all available secondary sources and, if they determine that it meets the thresholds, separately code its observable characteristics; any coding disagreements are resolved by moderated debate until the researchers reach consensus. The data cover 16 variables on movement characteristics, including mobilizing identities, organization, and action, for the 19 countries of the Middle East and North Africa from 1900-2012.