Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,274
datasets available to search
ShareScore release 0.7.1
Dataset results
7,274 results for “Comparative studies”
Dataset: The Role of News Consumption on Influencers' Facebook Pages in Threat Perception and Political Conservatism During Times of COVID-19: A Comparative Study between the USA, Spain, and Egypt
<p>Este archivo ofrece los datos en bruto de una encuesta examina el impacto del consumo de noticias en las páginas de Facebook de los influencers en la motivación del conservadurismo político durante amenazas como el terrorismo o las pandemias. Muestra: N=1309, jóvenes de entre 18 y 35 años en Estados Unidos, España y Egipto. Trabajo de campo realizado entre el 10 de agosto de 2021 y el 5 de septiembre de 2021.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación, Universitat Jaume I de Castellón</span></p>
Appendix - Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches
<p>The methods and results of the publication "COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.</p>
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Coverage-Dependent Stability of RuxSiy on Ru(0001): A Comparative DFT and XPS Study
<p>This repository contains the library of computational structures generated and used for our study "<span>Coverage-dependent stability of Ru<sub><span>x</span></sub>Si<sub><span>y</span></sub> on Ru(0001): a comparative DFT and XPS study</span>" (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CP04069D">https://doi.org/10.1039/D4CP04069D</a>). The final processed data is compiled into a single ASE-compatible database file (https://wiki.fysik.dtu.dk/ase/ase/db/db.html), `RuSi-PCCP-Data.db`.<br><br></p> <p> </p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Lethality datasets for "A comparative study of endoderm differentiation in humans and chimpanzees"
<p>These datasets were used to evaluate the embryonic lethality of 3 categories of genes: genes with shared reduction of variation in gene expression levels, genes with reduction of variation in only one species, and genes without a reduction of variation in either species.To obtain the data, we took the gene list of each of the 3 categories of genes and ran it through the Mammalian Phenotype database from Jackson Lab: <a href="http://www.informatics.jax.org/batch/summary">http://www.informatics.jax.org/batch/summary</a> in January 2018.</p>
Comparative Study of Entomotoxicity of Three Medicinal Plant Extracts against Sitophilus oryzae
<p>The Sitophilus oryzae is the most widespread and destructive primary stored cereals and grain pest in the world. The major effect of Sitophilus oryzae on an infestation by the feeding activity of grubs and adults and increasing the secondary growth of pests by making conditions optimum for optimum and further infestation. Plant extracts Azadirachta indica, Osmium Sanctum, and Mentha piperita were evaluated for Entomotoxicity such as repellency, adulticidal and larvicidal effect against Sitophilus oryzae. The Entomotoxicity of plant extracts expressed in percentage and Repellency were also expressed in class repellency with class 1, class 2, Class 3, Class 4, and class 5.TheRepellency with 80% of Class 4, Adulticidaland larvicidal percentage with 100 % of Azadirachta Indica and Adulticidal highest Entomotoxicity effect than Osmium sanctum and Mentha piperita</p>
Experimental plots studies comparing the impact of fertilization and wrack addition on invertebrate abundance and plant cover in Atlantic and Gulf of Mexico salt marshes from April 2009 to September 2010
Understanding the relative strengths of top-down and bottom-up forces is an important key to predicting the structure of biological communities. The strength of these effects can be regulated in part by predator abundance and nutrient availability. In 2009, we hypothesized that the importance of these factors varies geographically between the southeastern Atlantic Coast and the Gulf Coast due to differences in tidal regime, and began to study this variation using a biogeographic, manipulative field experiment. Although our original purpose was to understand the structure of salt marsh arthropod food webs, BP's Deepwater Horizon spill in the Gulf Coast presented an opportunity to understand how stress from an oil spill might affect the variables that we were measuring. The fact that we had plots and transect sampling in place at multiple sites along the Gulf and East Coasts put us in a position to evaluate any impacts that might occur if oil hit some of the sites. The study was conducted at 11 sites across the Gulf Coast, from Texas to Florida, and 11 sites along the Atlantic Coast, from Florida to Maine. At each site, experimental plots were sampled and a 100m transect was sampled near the plots within 5m of the high marsh boundary. Sampling was conducted in May 2009, August 2009, and August 2010. In 2010, four extra sites were added to the existing experimental sites because of known oil contamination, and another site was added as an extra control. Only the transect sampling was conducted at these sites. This dataset contains all the data from experimental plots; experimental treatments of fertilizer addition, wrack addition, fertilizer & wrack addition, and no addition (control) were randomly applied to the plots. The plot treatments were maintained in August 2009 and May 2010.
Comparative electrochemical study of veterinary drug – danofloxacin – at glassy carbon electrode and electrified liquid-liquid interface
<p>Data set for the paper " Comparative electrochemical study of veterinary drug – danofloxacin – at glassy carbon electrode and electrified liquid-liquid interface"</p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Dataset for "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties"
<p>Data of measurements and model output of the publication "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties". NO DOI YET</p> <p>The data consists of micrometeorological data, COS,CO<sub>2</sub> and H2O flux measurements and resistances of two soybean cultivars at an agricultural field in Italy.</p> <p>For additional information, please contact: <a href="mailto:felix.spielmann@uibk.ac.at">Felix.Spielmann@uibk.ac.at</a> or <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>.</p>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
Data for ms. Do people really care less about their cats than about their dogs? A comparative study in three European countries
<p>The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).</p> <p>Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25<sup>th</sup> of March 2022 in Austria, from 11-24<sup>th</sup> of March 2022 in Denmark and from 8-23<sup>rd</sup> of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey. </p> <p>Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.</p>
The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution
<p>Supplementary material for the article "The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution"</p> <p>- SI Figure 5: The World Asellidae phylogeny with credibility Intervals for the age of the nodes. Node labels of the phylogeny indicate the 95% credibility intervals of the estimated dates.</p> <p>- SI Table 1: Metadata for the 2093 COI sequences used in the study.</p> <p>- SI Table 4: Alignment of the 2093 COI sequences used for the delimitation of MOTUs.</p> <p>- SI Table 5: Alignment of the 424 COI sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 6: Alignment of the 424 16S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 7: Alignment of the 424 FASTKD4 sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 8: Alignment of the 424 28S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 9: Metadata for the DNA sequences used for the 4-gene dated phylogeny.</p> <p>- SI Table 11: Data on body size, sexual body size dimorphism, habitat specialization and habitat size used in comparative analyses.</p> <p>- SI Table 12: Metadata for the DNA sequences deposited in NCBI as part of this study.</p>
Enhancing Thermal Resilience in Delta Smelt: A Comparative Study of Temperature Regimes During Embryonic Development
This dataset contains larval heart rate measurements from hatchery-reared Delta Smelt (Hypomesus transpacificus), a critically endangered species native to the San Francisco Bay-Delta. The data were collected at the UC Davis Fish Conservation Physiology Lab as part of an experiment investigating how early-life exposure to different thermal environments influences cardiac performance and thermal tolerance. Embryos were incubated under three temperature regimes: constant (16°C), controlled diurnal fluctuation (16–20°C), and natural outdoor pond conditions with variable temperatures. Larval heart rate was recorded during an acute thermal ramp using a microscope-mounted camera system to track cardiac activity in real time. These data were collected to assess the physiological plasticity of Delta Smelt and to inform conservation hatchery strategies aimed at enhancing resilience to thermal stress.
Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set
<p>For a better understanding of magnetic field in the solar corona and dynamic activities such as flares and coronal mass ejections, it is crucial to measure the time-evolving coronal field and accurately estimate the magnetic energy. Recently, a new modeling technique called the data-driven coronal field model, in which the time evolution of magnetic field is driven by a sequence of photospheric magnetic and velocity field maps, has been developed and revealed the dynamics of flare-productive active regions. Here we report on the first qualitative and quantitative assessment of different data-driven models using a magnetic flux emergence simulation as a ground-truth (GT) data set. We compare the GT field with those reconstructed from the GT photospheric field by four data-driven algorithms. It is found that, at least, the flux rope structure is reproduced in all coronal field models. Quantitatively, however, the results show a certain degree of model dependence. In most cases, the magnetic energies and relative magnetic helicity are comparable to or at most twice of the GT values. The reproduced flux ropes have a sigmoidal shape (consistent with GT) of various sizes, a vertically-standing magnetic torus, or a packed structure with curled field lines. The observed discrepancies can be attributed to the highly non-force-free input photospheric field, from which the coronal field is reconstructed, and to the modeling constraints such as the treatment of background atmosphere, the bottom boundary setting, and the spatial resolution.</p>
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
<p><strong>A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:</strong></p> <p><strong><em> </em></strong></p> <p><strong><em>Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study</em></strong></p> <p> </p> <p> Iliana Chollett, D. Ross Robertson</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong>Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá</strong></p> <p><strong> </strong></p> <p>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 (6-33<sup>0</sup> N, 57-100<sup>0</sup> 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.</p> <p> </p> <p>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 <em>et al.</em> 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).</p> <p> </p> <p>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.</p> <p><strong> </strong></p> <p><strong>Global Biodiversity Information Facility </strong>(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).</p> <p> </p> <p><strong>Ocean Biogeographic Information System</strong> (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).</p> <p> </p> <p><strong>Integrated Digitized Biocollections</strong> (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).</p> <p> </p> <p><strong>FishNet2 </strong>(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).</p> <p> </p> <p><strong>FishBase</strong> (<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).</p> <p> </p> <p><strong>Smithsonian Tropical Research Institute</strong> (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.</p> <p> </p> <p>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 <em>Abudefduf saxatilis </em>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.</p> <p> </p> <p>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 <em>et al.</em>, 2013; Last <em>et al.</em>, 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%.</p> <p> </p> <p>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.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>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.</p> <p> </p> <p><strong>References</strong></p> <p><strong> </strong></p> <p>Allen, G.R. (1991) <em>Damselfishes of the World</em>. Mergus, Melle, 271 p.</p> <p>Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. <em>Scientific Reports</em>, 8, 4920.</p> <p>Carpenter, K.E. (Ed) (2002) <em>The living marine resources of the Western Central Atlantic.</em> Vols 1-3, FAO, Rome, 2127 p.</p> <p>Ebert, D.A., Fowler, S., Compagno, L. (2013) <em>Sharks of the World: a fully illustrated guide</em>. Wild Nature Press, Plymouth. 528 p.</p> <p>GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e).</p> <p>Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). <em>Marine Geodesy</em>, 5, 73–80.</p> <p>Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. <em>Atoll Research Bulletin</em>, 496, 611–624.</p> <p>Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). <em>Rays of the World</em>. CSIRO, Clayton. 790 p.</p> <p>Pattengill-Semmens, C.V. & Semmens, B.X. (2003) <em>Conservation and management applications of the reef volunteer fish monitoring program</em>. <em>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</em> (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht.</p> <p>Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. <em>Diversity and Distributions, 14<strong>,</strong> 891-892</em></p> <p>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. <em>Zookeys </em>843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a></p> <p>Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. <em>Smithsonian Tropical Research Institute, Balboa, Panamá</em>. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>.</p> <p>Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. <em>Journal of Geophysical Research: Solid Earth</em>, 101, 8741–8743.</p>
Data from Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies
<p>This dataset contains three text files in RIS format. They represent the screening process during study selection for "Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies" (<a href="https://doi.org/10.1111/liv.14179">https://doi.org/10.1111/liv.14179</a>). The file DOKU_All TiAb-Screening_20200116_cap contains all 3453 records (merged from original and update search) that were subjected to title-abstract screening. The file DOKU_All FT-Screening_20200116_cap contains all 174 records that were subjected to full-text screening. The file DOKU_All Included_20200116_cap contains all 26 records that were included into the final review.</p> <p>In addition, a PRISMA flow diagram (Fig. 1 in the paper) is available in TIF format.</p>
Churches, Arks of Migratory Narratives: A Comparative Study of the Greek-Orthodox Religioscapes in Germany and Great Britain
<p><strong>GO Religioscapes</strong></p> <p><em>The present research project deals with the Greek and Greek-Cypriot migrant communities in Germany and Britain, with reference to the religiocultural evidence found in the public sphere, which illustrate the particularities of their establishment and integration in the receiving country. As regards the Greek Gastarbeiter, they identified their communities with their parishes as the church often functioned as head of community and a mediator between them and the state. The bulk of the Greek-Cypriot Commonwealth migrants on the other hand, found the Greek-Orthodox Archdiocese already established as well, and as they expanded and dispersed across the British Isles, so did their parishes, which, in both cases, have served as arks of culture and identity. Therefore, one observes the phenomenon of interwoven migrant and church narratives; in the lapse of time, community and church, being closely knit, jointly constructed their migrant narratives of de- and reterritorialisation, cultural adaptation and hybridisation, essentially their own distinct sense of being and belonging. The particularities of this constantly under construction identity are manifest in the iconographical themes, aesthetics and concepts of their churches, which, albeit within canonical specifications, deviate from the normative typology as it is graphically attested by the occurrences of the phenomenon thereof. It is typical, however, of the Byzantine iconographic tradition to include and demonstrate the socio-political conditions of its time and place; and, those visual manifestations are part of a sociocultural reality as such, given that they possess a contextual dimension with reference to their symbolic content, their thematic endorsement and the appropriation of extra-ecclesiastical identity elements, but they are also an act and a medium of communication in their own right. It is therefore feasible to decode their aforementioned content and articulate the narrative that they convey.</em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.