Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm

Repository of the VR scene and the audio data used for the experiment reported in the publication <a href="https://ieeexplore.ieee.org/document/10269056" target="_blank" rel="noopener">available in Open Access</a>: <blockquote> Davide Fantini, Giorgio Presti, Michele Geronazzo, Riccardo Bona, Alessandro Giuseppe Privitera and Federico Avanzini (2023) "Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm" in IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue) </blockquote> The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/README.md">README.md</a> includes some instructions to use the data in this repository.   AUDIO The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> includes the Reaper's projects and audio files used in the experiment to provide the auditory stimuli (simultaneous reverberated speeches) to the participants. Each subfolder corresponds to a different Virtual Acoustics Environment (VAE): <ul> <li><LivingRoom|MARCo|METU> <ul> <li><Living Room|MARCo|METU>.rpp: Reaper's project for the VAE</li> <li>Bin: folder including the speech data convolved with the late reverberation part of the reverb condition \(B\) for each source position in the VAE</li> <li>Freeverb: folder including the speech data convolved with the late reverberation part of the reverb condition \(F_\text{d}\) for each source position in the VAE</li> <li>HOA: <ul> <li>ER: folder including the speech data convolved with the early reflections part (HOA in A-format) of the reference reverb condition \(H\) for each source position in the VAE</li> <li>Ref: folder including the speech data convolved with the entire reference reverb condition \(H\) (HOA in A-format) for each source position in the VAE</li> </ul> </li> </ul> </li> </ul> The reverberated speech data in the <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> file are obtained using third-party datasets: <ul> <li>The anechoic speech data are retrieved from four speakers (F2, F5, M3, M6) of the <a href="https://doi.org/10.5281/zenodo.6257551">ACE challenge corpus</a></li> <li>The Room Impulse Responses (RIR) in High-Order Ambisonics (HOA) format used to reverberate the speeches are retrieved from: <ul> <li><a href="https://doi.org/10.5281/zenodo.5747753">Living Room</a></li> <li><a href="https://doi.org/10.5281/zenodo.3477602">Concert hall (MARCo)</a></li> <li><a href="https://doi.org/10.5281/zenodo.2635758">Classroom (METU)</a></li> </ul> </li> </ul>   VR SCENE The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/VRscene.zip">VRscene.zip</a> includes the Virtual Reality (VR) scene provided to the participants during the experiment via an Oculus Quest 2. This file includes two subfolders: <ul> <li>UDPServer: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li>external/SharpOSC.dll: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li>VR_Headtracking: folder including the Unity project with the VR scene</li> </ul>

opencc-by-4.0Jun 2023View details →

Raw data: Specialized metabolites accumulation pattern in buckwheat is strongly influenced by accession choice and co-existing weeds

Screening suitable allelopathic crops and crop genotypes that are competitive with weeds can be a sustainable weed control strategy to reduce the massive use of herbicides. In this study, three accessions of common buckwheat Fagopyrum esculentum Moench. (Gema, Kora, and Eva) and one of Tartary buckwheat Fagopyrum tataricum Gaertn. (PI481671) were screened against the germination and growth of the herbicide-resistant weeds Lolium rigidum Gaud. and Portulaca oleracea L. The chemical profile of the four buckwheat accessions was characterised in their shoots, roots, and root exudates in order to know more about their ability to sustainably manage weeds and the relation of this ability with the polyphenol accumulation and exudation from buckwheat plants. Our results show that different buckwheat genotypes may have different capacities to produce and exude several types of specialized metabolites, which lead to a wide range of allelopathic and defence functions in the agroecosystem to sustainably manage the growing weeds in their vicinity. The ability of the different buckwheat accessions to suppress weeds was accession-dependent without differences between species, as the common (Eva, Gema, and Kora) and Tartary (PI481671) accessions did not show any species-dependent pattern in their ability to control the germination and growth of the target weeds. Finally, Gema appeared to be the most promising accession to be evaluated in organic farming due to its capacity to sustainably control target weeds while stimulating the root growth of buckwheat plants.

opencc-by-4.0Jun 2023View details →

Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"

Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595 List of files: Several folders corresponding to the figures of the paper. They contain: .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159). CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/). .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).

opencc-by-4.0Sep 2022View details →

Data from: Pathogen community composition and co-infection patterns in a wild community of rodents

ABSTRACT Rodents are major reservoirs of pathogens that can cause disease in humans and livestock. It is therefore important to know what pathogens naturally circulate in rodent populations, and to understand the factors that may influence their distribution in the wild. Here, we describe the incidence and distribution patterns of a range of endemic and zoonotic pathogens circulating among rodent communities in northern France. The community sample consisted of 713 rodents, including 11  host species  from diverse habitats. Rodents were screened for virus exposure (hantaviruses, cowpox virus, Lymphocytic choriomeningitis virus, Tick-borne encephalitis virus) using antibody assays. Bacterial communities were characterized using 16S rRNA amplicon sequencing of splenic samples. Multiple correspondence (MCA), regression and association screening (SCN) analyses were used to determine the degree to which extrinsic factors contributed to pathogen community structure, and to identify patterns of associations between pathogens within hosts. We found a rich diversity of bacterial genera, with 36 known or suspected to be pathogenic. We revealed that host species is the most important determinant of pathogen community composition, and that hosts that share habitats can have very different pathogen communities. Pathogen diversity and co-infection rates also vary among host species. Aggregation of pathogens responsible for zoonotic diseases suggests that some rodent species may be more important for transmission risk than others. Moreover we detected positive associations between several pathogens, including Bartonella, Mycoplasma species, Cowpox virus (CPXV) and hantaviruses, and these patterns were generally specific to particular host species. Altogether, our results suggest that host and pathogen specificity is the most important driver of pathogen community structure, and that interspecific pathogen-pathogen associations also depend on host species. FILE DESCRIPTION: MiSeq raw sequences of the 16Sv4 rRNA gene from spleen rodent samples This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each spleen rodent sample using the MiSeq platform. The 749 multiplexed PCR products were indexed using both forward and reverse indices. Information of the multiplexed samples (n=363 in replicate) and positive (n= 6) & negative controls (n= 17) is provided in the following XLSX file titled: 16S_raw_abundance_data.xlsx File name: MiSeq raw sequences of the V4 region 16S rRNA gene.zip Raw input and output files generated by the mothur program This ZIP file contains all the input and output files generated during the MiSeq sequence analysis with the mothur program. File name: Raw input and output files generated by the mothur program.zip Log file generated by the mothur program This TXT file contains is the history of all the command lines and parameters used during the MiSeq sequence analysis with the mothur program. File name: mothur.1428506786.logfile Raw abundance table of the 16v4 rRNA gene from spleen rodent samples before data filtering This XLSX file contains the number of reads for each distinct Operational Taxonomic Unit (OTU) and each of the PCR products, including the 332 spleen rodent samples analyzed in the study and the negative & positive controls, sequenced in the MiSeq run before the data filtering. This file contains also the following information: Study_site, Study_year, Sample_habitat, Host_species, Host_age, Host_sex, PCR_ID and the taxonomic classification (Kingdom to Genus) of each OTU. File name: 16S_raw_abundance_data.xlsx Occurrence table of the 16v4 rRNA gene from spleen rodent samples after data filtering This XLSX file contains the occurrences (presence: 1 ; absence: 0) after data filtering of each putative pathogenic Operational Taxonomic Unit (OTU) for each of the 332 spleen rodent samples analyzed in the study. File name: 16S_presence_absence_data.xlsx Statistical Analysis Scripts and Data File This ZIP file contains the R scripts for performing statistical analyses reported in the main text and supplemental materials. There is one main file (Analyses.R), as well as two source scripts required for association screening analyses (SCN.txt and FctTestScreenENV.txt). It also includes an R-legible data file containing occurrences (presence: 1 ; absence: 0) for all pathogen exposure variables on which statistical analyses were conducted (PA_DATA.csv) for each of the 332 spleen rodent samples analyzed in the study. The column names for bacterial exposures correspond to the “Pathogen Code” given in the 16S_presence_absence_data.xlsx file. File name: Statistical Analysis Scripts and Data File.zip

opencc-by-4.0Jul 2020View details →

Increased uptake of silica nanoparticles in inflamed macrophages but not upon co-exposure to micron-sized particles

Silica nanoparticles (NPs) are widely used in various industrial and biomedical applications. Little is known about the cellular uptake of co-exposed silica particles, as can be expected in our daily life. In addition, an inflamed microenvironment might affect a NP’s uptake and a cell’s physiological response. Herein, prestimulated mouse J774A.1 macrophages with bacterial lipopolysaccharide were post-exposed to micron- and nanosized silica particles, either alone or together, i.e., simultaneously or sequentially, for different time points. The results indicated a morphological change and increased expression of tumor necrosis factor alpha in lipopolysaccharide prestimulated cells, suggesting a M1-polarization phenotype. Confocal laser scanning microscopy revealed the intracellular accumulation and uptake of both particle types for all exposure conditions. A flow cytometry analysis showed an increased particle uptake in lipopolysaccharide prestimulated macrophages. However, no differences were observed in particle uptakes between single- and co-exposure conditions. We did not observe any colocalization between the two silica (SiO2) particles. However, there was a positive colocalization between lysosomes and nanosized silica but only a few colocalized events with micro-sized silica particles. This suggests differential intracellular localizations of silica particles in macrophages and a possible activation of distinct endocytic pathways. The results demonstrate that the cellular uptake of NPs is modulated in inflamed macrophages but not in the presence of micron-sized particles.

opencc-by-4.0Aug 2023View details →

Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials

Data extraction table for scoping review on best practices for co-creating patient-facing educational materials

openCC (other)Nov 2023View details →

Breeding Bird Community Surveys in the Huron Mountains, Marquette Co., Michigan (1997-1999).

Dr. Michael Kielb and collaborators conducted repeated surveys in June and July of 1997, 1998, and 1999, of breeding-bird communities along seven permanent transects in diverse habitats (old-growth forests, secondary forests, wetlands, riverine systems, etc.) within the boundaries of the Huron Mt. Club. Permanent 'listening-points' were established along each transect at intervals of ca. 200 m (7-20 points per transect), and numbers of singing birds tallied at each point. Data reported here are totals, by species, per transect. Detailed information on transect and point locations may be found in attached documents (reports to the Huron Mountain Wildlife Foundation) in .pdf format. Documents also include additional ad hoc observations. This study was repeated in 2020-2021, using the same transects and sampling points, by Ryan Buron and Harrison Jones, then graduate students at University of Florida; data from this follow-up study will be archived at EDI as a separate data-package.

openCC (other)May 2021View details →

Arthropod abundance in sweep net samples of sites near Crested Butte, CO in 2017-2020

The purpose of this study was to track year-to-year variation in arthropod communities of the host plant Ligusticum porteri (Apiaceae). In each study year (2017-2020), we used sweep nets to sample the arthropods at each of twenty sites containing populations of L. porteri. Sampling took place the end of June (June 28-30th), and we standardized sampling effort across years. Two field technicians netted insects from vegetation at a consistent pace for 30 seconds each. We sorted and identified insects to Order using a dissection microscope and a project-specific taxonomic key. Non-insect arthropods were also counted but not identified to additional taxonomic levels.

openOpenMar 2021View details →

Tree-Ring Data for Co-Occurring White Spruce and Paper Birch at an Intermediate Aged Stand in the Bonanza Creek LTER Regional Site Network - 2018

This dataset contains tree ring widths of co-occurring white spruce and Alaska paper birch. The data were published as part of a 2021 article in Journal of Ecology.

openCC0Mar 2023View details →

Soil biogeochemical responses to multiple co-occurring forms of human-induced environmental change

Multiple forms of human-induced environmental change are impacting arid ecosystems. Climate change is increasing temperatures and altering precipitation patterns, and many rapidly-growing urban centers are in arid locations. Nitrogen deposition from air pollution accompanies urban activities in many of these locations. These forms of environmental stressors will certainly impact soil communities and the biogeochemical processes for which they are responsible. However, most studies investigate these multiple environmental change factors independently or sometimes in pairs, but rarely all together as co-occurring forms of change. We examined how the simultaneous manipulation of increasing temperatures, altered precipitation patterns (both pulse size and frequency), nitrogen deposition, and urbanization influenced soil respiration and mineral N pools in the Sonoran Desert. In a laboratory microcosm, we incubated soils collected from an urban vs. exurban site, from plots receiving ~20 yrs of experimental N fertilization vs. control plots. The microcosm soils were incubated at ambient vs. +2 degree C temperatures under a factorial precipitation treatment of decreased frequency and increased pulse size. We measured the response of soil respiration rates and inorganic N pools to these co-occurring forms of environmental change.

openCustomJan 2020View details →

Monthly sea-level summary data for the Fort Pulaski, Georgia, water level station (NOAA/NOS CO-OPS ID 8670870) from 01-Jul-1935 to 30-Jun-2006

Monthly mean water levels based on MLLW (mean lower low water) datum in meters were acquired from the NOAA/NOS Center for Operational Oceanographic Products and Services web site (http://tidesandcurrents.noaa.gov/) for station ID 8670870 (Fort Pulaski, Georgia). Selected date/time and data columns were extracted from the CO-OPS web pages, standardized and documented using GCE-LTER metadata templates. This data set covers the period from 01-Jul-1935 to 30-Jun-2006

openCC (other)Sep 2019View details →

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Santa Barbara Co. FCD Eng. Bldg. (SBEngBldg234)

Precipitation was collected by the Santa Barbara County Flood Control District at Santa Barbara Co. FCD Eng. Bldg. (SBEngBldg234) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

opencc-by-4.0Jan 2020View details →

A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled: Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study Iliana Chollett1, D. Ross Robertson2 1 Sea Cottage, Louisburgh, Co. Mayo, Ireland 2 Smithsonian Tropical Research Institute, Balboa, Panamá

A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:   Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study                                    Iliana Chollett, D. Ross Robertson     Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá   This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-330 N, 57-1000 W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.   Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin et al. 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).   Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.   Global Biodiversity Information Facility (GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated  local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).   Ocean Biogeographic Information System (OBIS,  <a href="https://obis.org/">https://obis.org/</a>): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).   Integrated Digitized Biocollections (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).   FishNet2 (http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).   FishBase (<a href="http://www.fishbase.org/">http://www.fishbase.org</a>): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs  (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).   Smithsonian Tropical Research Institute (STRI; <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources:  data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and  Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO;   http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.   Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a perpetual existence. For example the damselfish Abudefduf saxatilis is a common and widespread inhabitant of tropical reefs on both sides of the Atlantic, but does not naturally occur outside that ocean. Despite the fact that its taxonomic status and range were resolved ~30 y ago (e.g. see Allen, 1991) museum data presented by the all five aggregators that contributed to the multi-source database used in this study currently (December 10, 2019) show large numbers of records of this species throughout the entire tropical Indo-Pacific, as well as across its native range in the Atlantic. Since many of the databases accumulating on aggregators are derivative (lists derived from records and from other derivative lists) it will become increasingly difficult to eliminate such errors as corrections to data in primary sources do not automatically propagate through the chain of usage by different databases. Due to increasing limitations on resources for taxonomic work, museums themselves have difficulty dealing with errors in specimen identity and location, and old specimens become unidentifiable, specimens never get returned when loaned out, or simply vanish, and entire collections can get destroyed by hurricanes or fires, or get dumped when museums close or experience a major change in mission. Georeferenced location data on fish distributions in the neotropics (and presumably most other areas) hosted by aggregators, particularly GBIF and OBIS, which take data from a broad range of source types, might best be described as messy, and the significant potential for errors in location records and an inability to verify records always needs to be taken into account when incorporating data from aggregators, primary museum sources, and analog sources.   Data considered for inclusion in the STRI database were screened as follows to exclude questionable records.  Data from two databases hosted by OBIS and GBIF were excluded entirely due to lack of reliability: BioGoMx (https://www.gulfbase.org/project/biodiversity-gulf-mexico-biogomx-database) and Diveboard (http://www.diveboard.com).  The only REEF data used were from “expert” REEF recorders on readily identifiable species that are unlikely to be confused with similar species (e.g. data for some genera of sparids, gerreids, labrisomids and gobies that include various sympatric species with very similar appearances, were not used).  After data from aggregators and museum sources were combined into a single database duplicate records were filtered out by rounding all records to three decimal places and eliminating duplicates, a process that inevitably deleted some valid records as well as duplicates. The sizes of the databases and abundance of such duplicates precluded individual manual exclusion. Finally, all location data for each species were revised by DRR by examining the distribution of its georeferenced coordinates overlayed on a digital map of the current known distribution range of that species (for such range information see Carpenter & De Angelis, 2002; Ebert et al., 2013; Last et al., 2016; Robertson & Van Tassell, 2019; IUCN Redlist species accounts for most species considered here: https://www.iucnredlist.org/search). Such revision took into account any recent modifications to taxonomy and distributions due to new data and new publications, or as a result of discussions between DRR and experts in the taxonomy of particular species or genera. Source information of many individual questionable records provided by aggregators with the hosted data was inspected to try and assess their validity. Records thought likely to be erroneous were deleted. Those included inexplicable records lacking adequate documentation located well outside the known distribution range, and records in unlikely habitats (e.g. on land for marine species; in deep water for shallow-water species). This revision process reduced the number of records by about 30%.   Data from the five individual aggregator databases that are used in the comparisons described here were all downloaded from their online portals during May, 2019. However, data from those five aggregators that were incorporated in the STRI database were downloaded in March 2017, with data from other sources described above added to the STRI database intermittently between then and May 2019, when the entire dataset was curated as described above. Hence the five individual aggregator databases analyzed in this study undoubtedly contain data not included in the version of the STRI database used in the present analyses.   Acknowledgements   Data acquisition and construction of the STRI database was supported by funds from STRI, the Smithsonian Marine Science Network, the Smithsonian Publications Fund, the Smithsonian’s Deep Reef Observation Project, the National Geographic Society, the IUCN Red List program, the Harte Research Institute, and CONABIO. We thank REEF and AGRRA for supplying species-location records, various people for taxonomic and location-record information used to construct that database (principal among them C Baldwin, S Brandl, K Conway B Frable, T Menut, T Munroe, R Robins, L Tornabene, J Van Tassell and B Victor), and hundreds of citizen-scientist submarine photographers whose images (see <a href="https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists">https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists</a>) acted as vouchers for location records.   References   Allen, G.R. (1991) Damselfishes of the World. Mergus, Melle, 271 p. Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. Scientific Reports, 8, 4920. Carpenter, K.E. (Ed) (2002) The living marine resources of the Western Central Atlantic. Vols 1-3, FAO, Rome, 2127 p. Ebert, D.A., Fowler, S., Compagno, L. (2013) Sharks of the World: a fully illustrated guide. Wild Nature Press, Plymouth. 528 p. GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e). Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). Marine Geodesy, 5, 73–80. Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. Atoll Research Bulletin, 496, 611–624. Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). Rays of the World. CSIRO, Clayton. 790 p. Pattengill-Semmens, C.V. & Semmens, B.X. (2003) Conservation and management applications of the reef volunteer fish monitoring program. Coastal Monitoring through Partnerships: Proceedings of the Fifth Symposium on the Environmental Monitoring and Assessment Program (EMAP) Pensacola Beach, FL, U.S.A., April 24–27, 2001 (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht. Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. Diversity and Distributions, 14, 891-892 Robertson, D.R,, Dominguez-Dominguez, O., Lopez Arollo, Y.M., Moreno Mendoza. R., Simoes, N. (2019) Reef-associated fishes from the offshore reefs of western Campeche Bank, Mexico, with a discussion of mangroves and seagrass beds as nursery habitats. Zookeys 843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a> Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. Smithsonian Tropical Research Institute, Balboa, Panamá. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>. Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research: Solid Earth, 101, 8741–8743.

opencc-by-4.0Jan 2020View details →

USP5 ZnF-UBD Co-Crystal Structure with Compound UBTR012574a

Objective: To grow well-diffracting co-crystals of USP5 zinc finger ubiquitin binding domain (ZnF-UBD) to solve the co-crystal structures to determine ligand interactions in the binding pocket and to determine if the predicted binding pose is similar to the experimental binding pose.

opencc-by-4.0Apr 2020View details →

Original dataset for "A validation of co-authorship credit models with empirical data from the contributions of PhD candidates"

Publication reference: Donner, P. (2020). A validation of co-authorship credit models with empirical data from the contributions of PhD candidates. Quantitative Science Studies, v. 1, i. 2, p. 551-564. <a href="https://doi.org/10.1162/qss_a_00048">https://doi.org/10.1162/qss_a_00048</a>.   The file contains one row per authorship contribution statement. Rows of publications and theses are grouped. Description of columns: dissertation_id - an integer identifying each dissertation thesis university - university at which the dissertation thesis was written and PhD degree conferred year - publication year of the dissertation thesis author - dissertation thesis author name title - dissertation thesis title subject - the field of research publication_id - an integer identifying each publication; publication associated with more than one thesis have the same id across theses reference - bibliographic reference for the publication associated with the thesis author_count - number of authors of the publication author_position - position in the author byline of the credited author credit - claimed credit of the author in percent corresponding_author - flag for whether the publication author of this row is a orresponding author

opencc-by-4.0Apr 2020View details →

CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NOx, SO2, CO, NH3, NMVOCs, BC, and OC from 1970-2017

CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NOx, SO2, CO, NH3, NMVOCs, BC, and OC from 1970-2017 version tag: 2020_v1.0 (April 2020) Annual anthropogenic emissions of 7 key atmospheric pollutants from 1970 - 2017, produced using the <a href="http://www.globalchange.umd.edu/ceds/">Community Emissions Data System</a>, updated for the Global Burden of Disease - Major Air Pollution Sources project (<a href="https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS">CEDS_GBD-MAPS</a>). Emissions are provided for NOx, SO2, CO, NH3, NMVOCs, Black Carbon (BC), and Organic Carbon (OC) from 11 anthropogenic sectors and four fuel categories as both annual country totals and global gridded emission fluxes (0.5 x 0.5 degree resolution). Note: The CEDS_GBD-MAPS inventory does not include emissions from open fires or aircraft. Sectors:  1. Agriculture (non-combustion sources only, excludes open fires) 2. Energy (transformation and extraction) 3. Industry (combustion and non-combustion processes) 4. On-Road Transportation 5. Off-Road/Non-Road Transportation (rail, domestic navigation, other) 6. Residential Combustion 7. Commercial Combustion 8. Other Combustion 9. Solvents 10. Waste (disposal and handling) 11. International Shipping Fuel Categories: 1. Total Coal Combustion (hard coal + brown coal + coal coke) 2. Solid Biofuel Combustion 3. Liquid Fuel (light oil + heavy oil + diesel oil) plus Natural Gas Combustion 4. CEDS Process Source Categories (see McDuffie, et al., (ESSD) 2020) for further details. Note: Total anthropogenic emissions = the sum of fuel categories 1-4 Zip File Details: The CEDS_GBD-MAPS inventory is available in three different formats: 1. CEDS_GBD-MAPS_annual_country_total_emissions_by_sector_fuel_1970-2017.zip <ul> <li>Zip file contains 7 .csv files that each contain a complete times series (1970-2017) of total annual anthropogenic emissions of each compound from each country, as a function of 11 anthropogenic sectors and 4 fuel categories.</li> <li>Emissions are in units of kt yr-1 and include NOx (as NO2), CO, SO2, NH3, total NMVOCs, BC, and OC</li> </ul> 2. CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_[year].zip <ul> <li>Each .zip file contains 145 netCDF files of annual anthropogenic global gridded emission fluxes, reported as a function of 11 anthropogenic sectors and 5 fuel categories (1 file per compound per fuel category, plus 1 file for the sum of all fuel categories)</li> <li>Emission fluxes are in units of kg m-2 s-1 and include NOx (as NO), CO, SO2, NH3, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provided as monthly averages and have been formatted for use in the GEOS-Chem model (<a href="http://acmg.seas.harvard.edu/geos/">http://acmg.seas.harvard.edu/geos/</a>).</li> <li>Example: ALD2-em-liquid-fuel-plus-natural-gas_CEDS_1970.nc inside the CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_1970.zip file provides monthly emission fluxes in 1970 for the subVOC ALD2 that result from the combustion of liquid fuel and natural gas in each of the 11 source sectors.</li> </ul> 3. CEDS_GBD-MAPS_[compound]_gridded_total_anthro_emissions_by_sector_input4CMIP_1970-2017.zip <ul> <li>compound = [BC_OC], [CO_NOx_SO2_NH3], [speciated_NMVOCs_01-04], [speciated_NMVOCs_05-08], [speciated_NMVOCs_09-14], [speciated_NMVOCs_15-18], [speciated_NMVOCs_19-22], or [speciated_NMVOCs_23-25]</li> <li>Each .zip file contains between 2 - 4 netCDF files (1 per compound) of anthropogenic global gridded emission fluxes from 1970-2017, as a function of 11 anthropogenic sectors only (no disaggregation of fuel categories)</li> <li>netCDF files follow the CEDS CMIP6 gridded emissions format. More information available at:  <a href="http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/">http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/</a></li> <li>Emission fluxes are in units of kg m-2 s-1 and include NOx (as NO2), CO, SO2, NH3, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provides as monthly averages</li> <li>Note: Zip files are group by compound only as a means to reduce the zipped file sizes. The file format for each compound is the same. </li> </ul>   *Additional data details are provided in the README.txt file*   *Version 2020_v1.0 of this dataset was produced to accompany the following manuscript: McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS), Earth System Science Data, Submitted

opencc-by-4.0May 2020View details →

Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH): outcome data

The datasets reports baseline and outcome data from the 'Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH)' experimental study. The study evaluated the effectiveness of a geriatric co-management programme on the cardiac care units of the University Hospitals Leuven. Sample included patients aged 75 years or older. Measurements included: demographic, functional status, cognitive status, depressive symptoms, anxiety symptoms, quality of life, physical performance, readmission rates, survival. Please see Word document for more information. Please see protocols for more information: <a href="https://clinicaltrials.gov/ct2/show/NCT02890927">https://clinicaltrials.gov/ct2/show/NCT02890927</a> <a href="https://bmjopen.bmj.com/content/8/10/e023593">https://bmjopen.bmj.com/content/8/10/e023593</a> The evaluation study is available at https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.17093

opencc-by-4.0Jun 2020View details →

Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine

Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».

opencc-by-4.0Jul 2020View details →

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-12.2019)

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources with weights: <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.   *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.