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Benchmark movement data set for trust assessment in human robot collaboration

In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016). Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants,  7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens: <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are: <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> This is followed by the trust questionaire items: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> The last two columns are <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>

MediaFutures Open Calls Data-Set

The Data-Set includes the data collected through the cascade funding open calls carried out during the H2020 MediaFutures project with the title “MediaFutures, Data-driven innovation hub for the media value chain”. The responsible and innovative use of data is instrumental in today's digitalised media industry. The EU-funded MediaFutures project has addressed this challenge by reshaping the media value chain. It has set up a virtual European data innovation hub to support entrepreneurial and innovative projects. It has also established a participatory inclusive innovation program encouraging synergies between businesses and creators and organised a competition to identify innovative digital entrepreneurs, creatives and data-empowered solutions. By delivering data and experimentation facilities to the winners, the project has showcased and improved their ideas. It has also facilitated the technical, legal, business and sustainability mentoring of businesses and artists and helped them achieve further access to funding. The virtual European data innovation hub has been supported by an international network of European organisations. The project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement 951962. The file contains data from the open calls, webinars, matchmaking events and help-desk activities carried out during the MediaFutures project. It includes data such as the number of applications received, data regarding eligibility and in-eligibility of applications, from which country the applications came, how many projects were evaluated and funded, data on the gender and ethnicity of applicants, etc. For more information and context related to the data, see also the following deliverables published on the MediaFutures website (https://mediafutures.eu/resources/): D1.4: Summary of Calls v1, D1.5: Summary of Open Calls v2, D1.6: Summary of Calls v3.

Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report

The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007–2011: Final Data Report, Kgs. Lyngby: National Museum of Denmark, 122 pp. The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report.  The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted. For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization". Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a>

Software Plagiarism Detection on Intermediate Representation Data Set

This data set contains the necessary files used for the bachelor's thesis Software Plagiarism Detection on Intermediate Representation. This includes the data sets for the tests, the implemented code and scripts for the evaluation as well as referenced work.

opencc-by-4.0May 2019View details →

Data Sets of Cause-Effect Pairs

Data Sets of Cause-Effect Pairs first used in the following paper: Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz IJCAI 2019 <pre><code>@inproceedings{Hassanzadeh19, author = {Oktie Hassanzadeh and Debarun Bhattacharjya and Mark Feblowitz and Kavitha Srinivas and Michael Perrone and Shirin Sohrabi and Michael Katz}, title = {Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts}, booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019, August 10-16, 2019, Macao, China}, year = {2019} }</code></pre> See README.txt for details. <pre>$ wc -l * 319 ce_me_benchmark_v1.csv 118 nato_sfa_benchmark_v1.csv 804 risk_models_benchmark_v1.csv 1730 semeval_benchmark_v1.csv 2971 total $ ls -lh * | awk '{print $5,$9}' 23K ce_me_benchmark_v1.csv 11K nato_sfa_benchmark_v1.csv 73K risk_models_benchmark_v1.csv 42K semeval_benchmark_v1.csv NATO SFA Benchmark is created from the tables in the Appendix of the following publicly available document: STRATEGIC FORESIGHT ANALYSIS 2017 REPORT Links: <a href="https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_hr.pdf">https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_hr.pdf</a> <a href="https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_txt.pdf">https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_txt.pdf</a> <a href="https://www.act.nato.int/futures-work">https://www.act.nato.int/futures-work</a> SemEval data is published under the Creative Commons Attribution 3.0 Unported license: <a href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</a> Details: <a href="https://docs.google.com/document/d/1QO_CnmvNRnYwNWu1-QCAeR5ToQYkXUqFeAJbdEhsq7w/preview">https://docs.google.com/document/d/1QO_CnmvNRnYwNWu1-QCAeR5ToQYkXUqFeAJbdEhsq7w/preview</a> Original source: <a href="https://drive.google.com/file/d/0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk/view?sort=name&layout=list&num=50">https://drive.google.com/file/d/0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk/view?sort=name&layout=list&num=50</a> The rest of the data sets are covered by the Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a> THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</pre>

dryad44/100

Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation

Open the record for dataset details and reuse information.

publicDec 2019View details →

edi44/100

Lake Sunapee Parameter Set and Driver Files for the General Lake Model Aquatic EcoDynamics (GLM AED)

Base driver data and parameter sets required for the General Lake Model - Aquatic EcoDynamics (GLM-AED) model simulation of Lake Sunapee, NH, USA. Additional files needed to run the simulation are the downscaled hourly meteorological data (North American Land Data Assimilation System NLDAS-2, website: disc.gsfc.nasa.gov), observed water temperature profiles and dissolved oxygen at the deep site in the lake (edi.463.1; doi.org/10.6073/pasta/846a36bf6fd704e508511e5f8a2ab3b5), and stream water temperature (edi.466.1; doi.org/10.6073/pasta/12a18d9d178b17d2ac333930f3efca9f).

openCC (other)Jun 2020View details →

edi44/100

PIE LTER geographic information regarding vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich and a reference marsh (Rough Meadows) in Rowley, Massachusetts.

A description of the vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich, MA and a reference marsh (Rough Meadows) in Rowley, MA.

openCC (other)Jan 2021View details →

opencc-by-4.0Dec 2019View details →

Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"

Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665 There are 2 files in this upload: 1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife. 2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.

opencc-by-4.0Dec 2019View details →

Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

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.

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.

Why, what and how do European healthcare managers use performance data? Results of a survey and workshop among members of the European Hospital and Healthcare Federation (Data set; anonymised)

The dataset presents results of a descriptive cross-sectional study based on a survey, delivered through an online self-reported questionnaire. The questionnaire was distributed to managers of hospitals and other health care organisations in a purposive sample of participants to the Exchange Programmes of the European Hospital and Health Care Federation (HOPE) eliciting information on the actual use of performance data in hospitals and other healthcare organisations in Europe in 2019. Data collected through the online questionnaire was analysed using univariate descriptive statistics. Analyses were conducted using the R statistical program version 3.6.1. Respondents were, for certain parts of the analysis, sub-grouped by their reported managerial position and experience, as well as the type of organisation they work for. Analysis was done on a full sample of respondents, including the primary, 2019 HOPE Exchange Programme participants, and the secondary study population, 2015-2018 Exchange Programme alumni and local hosts.

Example Eiger data with 6 virtual data sets dereferencing images

Standard Eiger data set from Diamond Light Source I04, consisting of 180° of rotation data from a cubic insulin crystal, with 6 virtual data sets each corresponding to 30° of data, to illustrate how multi-trigger Eiger data sets could be structured efficiently.    Virtual data sets made with    <a href="https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py">https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py</a>   Key feature is that the underlying data type (UINT16) corresponds to the virtual data set type.    This will require HDF5 1.10 series to read.    Files: insu_d200_1.nxs - NXmx formatted data with internal VDS insu_d200_1_000001.h5 - real data 1/2 insu_d200_1_000002.h5 - real data 2/2 insu_d200_1_1.nxs - VDS subset of data 1/6 ... insu_d200_1_6.nxs  - VDS subset of data 1/6 insu_d200_1_master.h5 - DECTRIS style master file insu_d200_1_meta.h5 - metadata insu_d200_1_meta_pack.h5 - repacked metadata (not used)

Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes

This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in Real-space Imaging of Confined Magnetic Skyrmion Tubes by M. T. Birch, D. Cortés-Ortuño, L. A. Turnbull, M. N. Wilson, F. Groß, N. Träger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Schütz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gräfe and P. D. Hatton. Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries. Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper. Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class T: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t) The analysis of OOMMF's output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice. Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.   Latest version of this Data Set can be found at the Github repository: <a href="https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes">https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes</a>

TIPP 2.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63

Software: TIPP SoftwareVersion: 2.0.0 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/smirarab/sepp DockerImage: stefanjanssen/docker_profiling_tools:tipp IsBiobox: True BioboxYAMLFile: https://zenodo.org/record/3629567/files/biobox.yaml?download=1 ReferenceDatabase: 2015 ShortReadsUsed: True LongReadsUsed: False CommandsUsed: docker run \ --volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \ --volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \ --volume="/path/to/output:/bbx/mnt/output:rw" \ --volume="/path/to/output/metadata:/bbx/metadata:rw" \ --volume="/path/to/output/cache:/cache:rw" \ stefanjanssen/docker_profiling_tools:tipp

Bracken 2.5 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63

Software: Bracken SoftwareVersion: 2.5 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/jenniferlu717/Bracken DockerImage: cami/bracken:2.5 IsBiobox: True BioboxYAMLFile: https://zenodo.org/record/3629567/files/biobox.yaml?download=1 ReferenceDatabase: Kraken standard db built May 2019 ShortReadsUsed: True LongReadsUsed: False CommandsUsed: docker run \ --volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \ --volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \ --volume="/path/to/output:/bbx/mnt/output:rw" \ --volume="/path/to/output/metadata:/bbx/metadata:rw" \ --volume="/path/to/output/cache:/cache:rw" \ cami/bracken:2.5

CAT 4.6 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: CAT SoftwareVersion: 4.6 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/dutilh/CAT ReferenceDatabase: prebuilt 2018-12-12 Taxonomy: NCBI 2018-12-12 ShortReadsUsed: False LongReadsUsed: False CommandUsed: CAT contigs -c anonymous_gsa_pooled.fasta -d CAT_prepare_20181212/2018-12-12_CAT_database/ -t CAT_prepare_20181212/2018-12-12_taxonomy/ --tmpdir tmp --nproc 16

PhyloPythiaS+ 1.4 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: PhyloPythiaS+ SoftwareVersion: 1.4 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/algbioi/ppsp DockerImage: cami/ppsp:1.4 IsBiobox: False ReferenceDatabase: RefSeq 93, SILVA 132 Taxonomy: NCBI 2018-02-26 ShortReadsUsed: False LongReadsUsed: False CommandUsed: run_ppsp.py --pipelineDir ppsp_pipepline --inputFastaFile anonymous_gsa_pooled.fasta --databaseFile ncbi_taxonomy --refSeq refseq93 --s16Database SILVA_132 --mgDatabase reference_NCBI201502/mg5

MetaPalette 1.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63

Software: MetaPalette SoftwareVersion: 1.0.0 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://doi.org/10.5281/zenodo.1730624 DockerImage: stefanjanssen/docker_profiling_tools:commonkmers IsBiobox: True BioboxYAMLFile: https://zenodo.org/record/3629567/files/biobox.yaml?download=1 ReferenceDatabase: https://zenodo.org/record/1749272 ShortReadsUsed: True LongReadsUsed: False CommandsUsed: docker run \ --volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \ --volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \ --volume="/path/to/output:/bbx/mnt/output:rw" \ --volume="/path/to/output/metadata:/bbx/metadata:rw" \ --volume="/path/to/output/cache:/cache:rw" \ --volume="/path/to/reference_database:/exchange/db:rw" \ stefanjanssen/docker_profiling_tools:commonkmers