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3,225 results for “Case of study”
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
Creatures case studies (data)
<p>This data set contains case studies of creative practices that aim to produce transformations towards sustainability identified during the Creatures project</p>
TRANSIT long-distance multimodal trips model results - a Spanish case study
<p>The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>).</p> <p>An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>.</p> <p>The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios:</p> <ol> <li><strong>CS01 - Baseline</strong>. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li><strong>CS02 - HSR connection with Madrid-Barajas airport</strong>. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li><strong>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation</strong>. The effects of the timetable synchronisation are modelled.</li> </ol> <p>Each scenario includes the following files:</p> <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> <p>The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. </p>
Golden Agents - Processes of Creativity (case study)
<p>We can observe a 'rise' of or 'renewed interest' in the illustrated book in the second half of 17th century and the beginning of the 18th century. New genres and techniques and an increasing competition in the book market lead to new collaborations between authors, printers, and illustrators. It would be interesting to track these producers who collaborated on illustrated book projects and trace whether these commercial collaborations were grounded or resulted in social and/or religious interactions. For this, we are bringing together several datasets in the Golden Agents infrastructure that each contain information on the production of books, or on social relations between actors involved in the production process.</p> <p>Aim of case study:</p> <ul> <li>Linking & querying different datasets to find overarching patterns on creative industries in the Dutch Republic</li> </ul> <p>Overarching Research Question:</p> <ul> <li>How can we map innovation in cultural production?</li> <li>What is the relation between different sorts (bonding & bridging) of creative and social interactions?</li> </ul> <p>This release (v0.9) marks the state of the data and case study at the end of the Golden Agents project (2016-2022).</p>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset
<p>This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article "Dissemination of information in event-based surveillance, a case study of Avian Influenza".</p> <p>The reports were published between 1 July 2018 and 30<sup>st</sup> June 2019 and described one or several avian influenza outbreaks. We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official.</p> <p>In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter <em>S<sub>E</sub></em> and receptor sources <em>S<sub>R</sub></em>. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized).</p> <p>The script for data analyses is available at https://github.com/SarahVal/EBS-network.</p>
ipaast-czo case study: Dehesa Boyal de Botija
<p>VV01 (“Cerca del cementerio” sector). Data set description.</p> <p>One single geophysical method was used:</p> <ul> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Although topography obliged to split the survey area in 3 independent data sets, after correction and merging of all the data in one single data set it was observed that, considering the small time lapse between each one and homogeneous weather conditions, no significant discontinuity in measured data was observed.</li> </ul> <p>The interest on testing EMI survey in this dehesa environment within the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV01 sector corresponds to an area of dehesa named “Cerca del cementerio” (“cemetery enclosure”): it is a land plot 70m SW of the hillfort with a surface of 5240 sq. m. Previous knowledge of the area revealed the presence of a high density of archaeological materials, but there was no clear evidence of buried structures. A particular feature of this sector was the great depth of soil deposits within the walled enclosure, in great contrast with the surrounding fields. Data sets:</p> <ul> <li> EMI04. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> <p>VV02 (Mercadillo sector). Data set description.</p> <p>Two main methods were used:</p> <ul> <li>Magnetic survey: a dual-sensor gradiometer system (Grad602 Bartington)</li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Dense vegetation and topography obliged to split the survey area in 3 independent data sets.</li> </ul> <p>Additionally several areas were surveyed with GPR (Nogging system of Sensor&Software).</p> <p>The interest on testing different survey methods in the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV02 sector corresponds to an area of dehesa named “El Mercadillo (“little market”). It is located 150 m south of the hillfort of Villasviejas and covers a surface of approx. 3500 sq m. In this sector was excavated in the 1980s a funerary area corresponding to the earliest period of the hillfort (IV-II centuries B.C). Analysis of LiDAR data revealed that in the same area there were several topographic features that could be linked with off-site activity during the protohistoric and early roman period. Data sets:</p> <ul> <li> EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li> EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>EMI03. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>MAG01. <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> <p> </p>
ipaast-czo case study: The Rinconada Estate.
<p>The interest in testing different survey methods within the framework of the IPAAST project was motivated by three objectives:</p> <ul> <li>identifying evidence of rural life in the hinterland of the Roman colony of Augusta Emerita, with special attention to forms of resilience and diversification of agrarian activities beyond the areas of highest productivity of the alluvial plain of the Guadiana River.</li> <li>assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and perform a regression analysis to assess the potential distribution of arable lands in the area from Roman times to the present.</li> </ul> <p>combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</p> <p> </p> <p>Attention was focused on a sector of the estate where two elements were coincident 1) preliminary evidence suggested the estate’s highest concentration of archaeological finds. 2) land plots within the estate used for grazing where LIFE Adapt experiments were undertaken. This area encompassed approximately. 6.5 ha.</p> <p> </p> <p>2 geophysical methods for the exploration were used:</p> <ul> <li>Magnetic survey: a 2 sensors gradiometer system was used (Grad602 Bartington). Data sets: <ul> <li>RC_MAG <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> </li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Data sets: <ul> <li>RC_EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data (raw data)</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>RC_EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
ipaast case study: Wiltshire yield and EM survey data
<p>These data were collected by Soyl Ltd as part of a commercial agricultural soil survey. They were reprocessed by the University of Ghent ipaast team. </p>
ipaast-czo case study OptRX data: Montalcino (SI, Italy)
<p>These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture.</p> <p>The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: </p> <p> </p> <p>NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE</p> <p> </p> <p>The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). </p> <p>To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. </p> <p>Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster. </p>
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
<p>Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).</p> <p>The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. </p> <p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p> <p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.</p> <p>A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu.</p> <p><strong>Description of data provided</strong></p> <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. <em>Carmakers’ Race to Meet the</em>, <em>21</em>.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>
From genomics to integrative species delimitation? The case study of the Indo-Pacific Pocillopora corals
<p>With the advent of genomics, sequencing thousands of loci from hundreds of individuals now appears feasible at reasonable costs, allowing complex phylogenies to be resolved. This is particularly relevant for cnidarians, for which insufficient data is available due to the small number of currently available markers and obscures species boundaries. Difficulties in inferring gene trees and morphological incongruences further blur the study and conservation of these organisms. Yet, can genomics alone be used to delimit species? Here, focusing on the coral genus <em>Pocillopora</em>, whose colonies play key roles in Indo-Pacific reef ecosystems but have challenged taxonomists for decades, we explored and discussed the usefulness of multiple criteria (genetics, morphology, biogeography and symbiosis ecology) to delimit species of this genus. Phylogenetic inferences, clustering approaches and species delimitation methods based on genome-wide single-nucleotide polymorphisms (SNP) were first used to resolve <em>Pocillopora</em> phylogeny and propose genomic species hypotheses from 356 colonies sampled across the Indo-Pacific (western Indian Ocean, tropical southwestern Pacific and south-east Polynesia). These species hypotheses were then compared to other lines of evidence based on genetic, morphology, biogeography and symbiont associations. Out of 21 species hypotheses delimited by genomics, 13 were strongly supported by all approaches, while six could represent either undescribed species or nominal species that have been synonymised incorrectly. Altogether, our results support (1) the obsolescence of macromorphology (i.e., overall colony and branches shape) but the relevance of micromorphology (i.e., corallite structures) to refine <em>Pocillopora</em> species boundaries, (2) the relevance of the mtORF (coupled with other markers in some cases) as a diagnostic marker of most species, (3) the requirement of molecular identification when species identity of colonies is absolutely necessary to interpret results, as morphology can blur species identification in the field, and (4) the need for a taxonomic revision of the genus <em>Pocillopora</em>. These results give new insights into the usefulness of multiple criteria for resolving <em>Pocillopora</em>, and more widely, scleractinian species boundaries, and will ultimately contribute to the taxonomic revision of this genus and the conservation of its species.</p> <p> </p> <p>This deposit contains the data related to Oury N, Noël C, Mona S, Aurelle D, Magalon H (2023) From genomics to integrative species delimitation? The case study of the Indo-Pacific <em>Pocillopora </em>corals. Mol Phylogenet Evol 107803. doi:10.1016/j.ympev.2023.107803</p> <p>See 0_README.txt for more content details.</p>
Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm
<p>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>:</p> <blockquote> <p>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 <em>IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue)</em></p> </blockquote> <p>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.</p> <p> </p> <p><strong>AUDIO</strong></p> <p>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):</p> <ul> <li><<em>LivingRoom</em>|<em>MARCo</em>|<em>METU</em>> <ul> <li><<em>Living Room</em>|<em>MARCo</em>|<em>METU</em>><em>.rpp</em>: Reaper's project for the VAE</li> <li><em>Bin</em>: 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><em>Freeverb</em>: 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><em>HOA</em>: <ul> <li><em>ER</em>: 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><em>Ref</em>: 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> <p>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:</p> <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> <p> </p> <p><strong>VR SCENE</strong></p> <p>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:</p> <ul> <li><em>UDPServer</em>: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li><em>external/SharpOSC.dll</em>: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li><em>VR_Headtracking</em>: folder including the Unity project with the VR scene</li> </ul> <p> </p>
SPEChpc 2021 Benchmarks: A Performance and Energy Case Study
SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids based Infiniband Clusters: A Performance and Energy Case Study.
Report on Transformers interpretability for Natural Language Processing: A case study on Technical Debt classification
<p>Transformer models have significantly advanced the field of natural language processing (NLP), achieving exceptional results in various tasks. However, these models are often seen as "black boxes", providing limited insight into the factors influencing their predictions. It has become crucial to develop and utilise methods for interpreting and explaining these models to uncover their complex inner workings. This report discusses the latest techniques and tools that aid in a more profound understanding of transformer models within NLP. Additionally, it explores a vital industrial use case: Technical Debt (TD) classification. In this context, the report leverages transformer model interpretability tools and Retrieval Augmented Generation (RAG) to analyse and understand the characteristics of text in Github issues, distinguishing between TD and non-TD.</p> <p>This report thoroughly outlines an approach to improve the transparency and reproducibility of machine learning models, with a special emphasis on TD classification. It integrates the RAG approach and exploits feature attribution techniques, presenting a route to create AI systems that are not only high-performing but also demonstrably trustworthy and comprehensible. Through a detailed examination of word patterns in TD classification and the innovative use of the RAG approach, the research highlights a strong dedication to promoting transparency and responsibility in AI systems, potentially ushering in a new phase in machine learning research that focuses on clarity and dependability.</p>
Film Programming in the USSR: A Case Study of Moscow Cinemas (1946–1955)
<p>This paper presents a database on film programming in Moscow cinemas between 1946 and 1955. It outlines the place of this research at the intersection of new cinema history and academic debates on film distribution in the field of Soviet history. The paper describes the data collection, the coding to present the data, and the structure of the database, which consists of the three datasets on Moscow film programming (1946–1955), Moscow cinemas (1946–1955), and the 1952 film calendar. Concluding remarks summarize the knowledge obtained from the database and introduce the Soviet case into the international context of digital data collections for historical cinema studies.</p>
User Study Data for Paper "A case study in designing trustworthy interactions: implications for socially assistive robotics"
<p>Experimental data collected for the user study described in Frontiers paper "A case study in designing trustworthy interactions: implications for socially assistive robotics" by Mengyu Zhong et al. Citation: <i>Zhong, Mengyu, et al. "A case study in designing trustworthy interactions: implications for socially assistive robotics." Frontiers in Computer Science 5.1152532 (2023). </i></p>
Termite casing data from the long-term Small Mammal Exclosure Study (SMES) at Jornada Basin LTER, 1995-2005
This data package contains termite activity data in plots with a range of herbivore exclusion treatments on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Study sites were established in 1995; one in black grama grassland and the other in creosotebush shrubland to compare the impact of herbivores on ecosystem processes between these vegetation types. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Each study site is 1 km by 0.5 km in area. Four replicate experimental blocks were randomly located at each study site to measure vegetation responses using exclusion treatments including a) all mammalian herbivores, including cattle, lagomorphs, and rodents, b) lagomorphs and cattle only, c) cattle only, and d) control accessible to all herbivores. Thirty-six sampling points were positioned at 5.8-meter intervals on a systematically located 6 by 6 point grid within each plot. A permanent one-meter by one-meter vegetation measurement quadrat is located at each of the 36 points. Each spring and fall from 1995-2005, a tape measure was used to measure the length, diameter, and height in centimeters of each termite casing in these vegetation quadrats. This study is complete.
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>
Fig. 29 in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)
Fig. 29. Current distribution of Streblopus van Lansberge, 1874 and the Old World groups with which it is believed to be more closely related plotted on an Upper Cretaceous palaeomap (~ 80 million years ago). Based particularly on the hypothesis in Tarasov & Génier (2015) that Streblopus is part of a clade otherwise composed uniquely of dung beetle lineages either exclusively distributed in Africa (Circellium, Chalconotus and Gyronotus) or with a distribution largely centred on that continent (Scarabaeini), and on the dating of the origin of the Scarabaeini as 71 million years ago (Gunter et al. 2016), we propose that the lineage that would eventually lead to Streblopus branched off from those groups in Africa some time between 95 and 71 million years ago, and that one of its descendent lineages (the only one living today) dispersed from its original continent to South America during the late Upper Cretaceous or the early Cenozoic. Since Africa and South America have not been connected by land since the Lower Cretaceous, the only way the ancestor of Streblopus could have reached South America was through transoceanic dispersal across the early South Atlantic. That dispersal probably happened by rafting on floating pieces of plants or other debris, as probably occurred with a large number of other organisms. Palaeomap modified from Scotese (2016); distribution area based on Balthasar (1963), Scholtz & Howden (1987), Davis et al. (2008) and our own results.
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