Find research datasets worth reusing

GIS13 GIS Coverages Defining Konza Wildfire and Supplementary Burn History (1977-present)

This dataset contains a comprehensive record of supplemental burns, wildfires, wildfire cleanup burns for the Konza Prairie Biological Station (KPBS) dating from 1972. Burn history data contains date burned, area burned and type of treatment (wildfires, wildfire cleanup, and supplemental burns). Burn histories for planned, prescribed burns are available in dataset GIS05. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).

GIS19 A GIS Coverage Defining Permanent Structures on Konza Prairie (1977-present)

This dataset defines the permanent buildings located on the Konza Prairie Biological Station (KBPS). The data include building names and addresses. These data are available as zipped (.zip) shapefiles (.shp).

GIS20 GIS Coverages Defining Konza Elevations

These data depict the elevation features of Konza Prairie. Record type 1 is a 2 meter resolution digital elevation model (DEM) of Konza Prairie, generated from 2006 LiDAR DEM data collected to standard USGS specifications (GIS200). Record type 3 is a 2010 10 meter (1/3 arc second) resolution National Elevation Dataset (NED) DEM of Konza Prairie (GIS202). Record type 4 is a 10 meter resolution NED DEM of Konza Prairie with a modified 3 kilometer buffer (GIS203). Record type 5 is a USGS topographic map of Konza Prairie (GIS204). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS21 GIS Coverages Defining Water Bodies on Konza Prairie (1972-present)

This Coverage Contains the Locations of Streams (GIS210) and Waterbodies (GIS211) within the Konza Prairie Biological Station. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS22 GIS Coverage Defining Soils (SSURGO) on Konza Prairie (1982-present)

The Konza Prairie soils dataset is derived from the USDA NRCS SSURGO soils definitions for Riley and Geary Counties (variant ca. 2012; soildatamart.nrcs.usda.gov/). The coverage contains MUSYM and Soil Names that correspond to the code. Additional and current SSURGO data is available from (soildatamart.nrcs.usda.gov/SSURGOMetadata.aspx) Associated metadata derived from NRCS SSURGO Metadata for: Riley County SSURGO Data - soildatamart.nrcs.usda.gov/Metadata.aspx?Survey=KS161&UseState=KS Geary County SSURGO Data -soildatamart.nrcs.usda.gov/Metadata.aspx?Survey=KS061&UseState=KS. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS30 GIS Coverages Defining Sample Locations for Abiotic Datasets on Konza Prairie (1972-present)

These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS309 defines the locations within watershed N4D of soil sampler nests. In Jan 2020, we separated the original GIS310 file 'Wells in N4D' into GIS310 'Wells in N4D' and GIS309 'Soil Sampler Nests'. Prior to then, soil sampler nests and wells were combined in GIS310. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream sampling stations within multiple Konza watersheds. These data are to be used in conjunction with the NWC, ASS, ASD, and ASW datasets. GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

GIS35 GIS Coverages Defining Sample Locations for Belowground Datasets on Konza Prairie (1982-present)

These data show the locations of research conducted at the below ground plots near Konza Headquarters. Record type 1 (GIS350) describes the 64 belowground plots receiving a variety of nutrient, burn, and mowing treatments. Data for BMS01, BMS02, and BNS01 are collected on these plots. Record type 6 (GIS355) describes the locations of the Micro-Rhizotrons. Two spatial datasets lie on the belowground plots, but are classified separately. These are the Lysimeters on belowground plots (GIS455) and Aboveground biomass on belowground plots (GIS505) datasets. GIS505 may be used alongside the BGPVC dataset, because it shares sample locations with PBB01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS40 GIS Coverages Defining the Sample Locations of Konza Consumer Data (1982-present)

These data show the sampling locations for the consumer datasets at Konza Prairie. GIS400 defines the starting points for sweep samples of grasshoppers across Konza Prairie. These data may be used in conjunction with the sweep sample datasets (CGR02). GIS401 defines the starting points for sweep samples of grasshoppers across Konza Prairie, focusing on grazing impact. These data may be used in conjunction with the sweep sample datasets (CGR02Z). GIS405 defines the trap locations for small mammal sampling across Konza Prairie. These data may be used in conjunction with CSM0X. GIS 406 defines the locations of small mammal host parasite sampling at Konza Prairie. These data may be used in conjunction with CSM08. GIS410 defines the stream stretches for fish sampling across Konza Prairie. These data may be used in conjunction with CFC01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS45 GIS Coverages Defining the Konza Nutrient Data Sample Locations (1982-present)

These data show the sample locations for soil bulk density and chemical characteristics along LTER vegetation plots. This dataset contains the transect lines (GIS450) and sample locations(GIS451) at which the soil cores are sampled. These data may be used in conjunction with the Soil Chemistry and Bulk Density (NSC01) datasets. GIS455 contains the locations of the lysimeters used to measure soil water chemistry on the belowground plots. These data may be used in conjunction with the NBS01 dataset. GIS460 contains the locations of the bulk precipitation collectors on Konza Prairie. These data may be used in conjunction with the NBP01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS55 GIS Coverages Defining the Konza HQ Irrigation System (1982-present)

These data show the components of the irrigation system near Konza Prairie HQ. Record types 1, 2, 3 and 4 demarcate the locations of the study plots heads (GIS550), transect lines (GIS551), irrigation lines (GIS552), and irrigation line joints (GIS553). Record types 4 and 5 describe the location of the storage piles (GIS554) and the irrigation reservoir (GIS555). This data may be used in conjunction with the Irrigation Transect Studies (WATXX) data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS60 GIS Coverages Defining Other Konza Sample and Research Areas (1982-present)

These data show locations of samples and research areas at Konza that do not fit under our standard classifications. GIS 600 contains the locations of the Hulbert plots on Konza Prairie. GIS605 contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. GIS 610 provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. GIS615 contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. GIS620 contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. GIS630) and GIS635 contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

opencc-by-4.0Nov 2023View details →

Selkie GIS Techno-Economic Tool input datasets

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in theIrish and Welsh maritime areas. Read more here:https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/ <blockquote>This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.</blockquote> ********************File Formats********************Results are presented in three file formats: tif Can be imported into a GIS software (such as ARC GIS)csv Human-readable text format, which can also be opened in Excelpng Image files that can be viewed in standard desktop software and give a spatial view of results  ******************Input Data******************All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data. Hourly Data from 2000 to 2019 - Wind -Copernicus ERA5 dataset17 by 27.5 km grid  10m wind speed - Wave -Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset3 by 5 km grid  *********************Accessibility*********************The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.  The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month. Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if  the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month. Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.  Wind hourly data is from the ERA 5 dataset.    ********************Availability********************A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather  windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the  relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation betweenaccessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer. The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the  environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.  Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined  by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total  number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationshipbetween the two. A mature technology reliability was assumed.  **********************Weather Window**********************The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the  given duration for the month. The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset  (0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer. The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.  The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep? Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather  windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain  suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable  weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available  at any given point in the month. *****************************Extreme Wind and Wave*****************************The Extreme wave layers show the highest significant wave height expected to occur during the given return period.The Extreme wind layers show the highest wind speed expected to occur during the given return period.   To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced  portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model  that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values  for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold. The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution. The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). Thesecond input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected  extremes and used to calculate the extreme value for the selected return period.

opencc-by-4.0Jan 2022View details →

GIS Protocol for Multy-Scale Emerging Hot Spot Analysis

This GIS protocol is primarily intended as supplementary material to the article (Štular et al., 2022). The article contains important contextual information about its intended use. In short, this GIS protocol was developed for the purposes of archaeological regional analysis of spatial data. The data are provided elsewhere in spreadsheet format (Štular et al., 2021). Data in GIS format are included in this repository. The GIS protocol can be used with any relevant data for any purpose as long as the data format matches the format of the included data. Includes GIS protocol (textual description) and GIS data in *.shp format.

opencc-by-4.0Mar 2022View details →

First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS

This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI). Abstract: Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.

opencc-by-4.0Mar 2022View details →

MSCA-IF-896925_MaMo_WP2-A_GIS

This dataset originates from the research activities (WP2-WP3) carried out during the H2020- MSCA-IF 2019 EU-funded project "Materializing Modernity - Socialist and Post-socialist Rural Legacy in Contemporary Albania (MaMo)", GA. no. 896925, implemented by Federica Pompejano (MSCA-IF Researcher) at the Instituti i Antropologjise Kulturore dhe i Studimit te Artit (Akademia e Studimeve Albanologjike), Tirana, Albania. This dataset contains five .qgz files and a set of georeferenced raw data (JPG pictures and GPS tracks) collected during the MaMo fieldwork research activities (WP3). This dataset has been curated by Dr Federica Pompejano. The mapping of buildings and landscapes carried out during the fieldwork activities aimed at providing extensive visual documentation of the rural landscape of the five representative macro-areas selected for the MaMo research project. The mapping activity was carried out by means of the QFIELD App and then synchronized in QGIS Desktop 3.18.2. The QGIS Projects (WP2) have been created by Dr Federica Pompejano (IAKSA-ASA) in collaboration and with the support of Prof. Bianca Federici (Università di Genova - UNIGE) and Dr Ilaria Ferrando (Università di Genova - UNIGE). Unless otherwise specified, the data contained in this dataset are open for public disposal under the terms and conditions described in the CC BY-NC-SA 4.0 license. A copy of each QGIS project is deposited at the Scientific Archive of Ethnography and Folklore (IAKSA-ASA) where all MaMo materials are stored, preserved, and are available for consultation under the Archive's terms and conditions. The Scientific Archive of Ethnography and Folklore (IAKSA-ASA) is in Rruga Naim Frasheri 1, Tirana (Albania). E-mail contact is: iaksa@asa.edu.al. ACKNOWLEDGMENTS: This dataset and all its content are part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896925.

opencc-by-4.0Apr 2022View details →

Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions

Introduction Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results. The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets: (1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder. (2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro. (3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder. Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific. More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.   Data processing We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.   Version Version 2022.1.   Acknowledgements This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.   Citation Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision   Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940   Contacts Dr. Jie LIU: E-mail: <a>liujie@mail.kib.ac.cn</a>; Mr. Guangfu ZHU: <a>zhuguangfu@mail.kib.ac.cn</a> Institution: Kunming Institute of Botany, Chinese Academy of Sciences Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China   Copyright This dataset is available under the Attribution-ShareAlike 4.0 International (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>).

opencc-by-4.0Dec 2017View details →

GIS data for Bracciano Smart Lake Initiatives

Gis Data relative to lake of bracciano ( RM) supporting Bracciano SmartLake initiative managed by Emanuele Perugini & Guido Tocco. Author: Alfonso Crisci IBIMET CNR and & Massimo Perna Consorzio LaMMA Data: Perimetral ISTAT administrative boundaries of Bracciano's Area, Bracciano Lake Catchment basin and 5 meter DEM of area ( digital elevation model) obtained troughout TIN interpolation by using elevation data ( CTR5K) retrieved from OpenData Lazio WEB Portal .

opencc-by-4.0May 2024View details →

Tawi Said Archaeological Survey: GIS

This is part of the data (GIS) obtained during the survey at Tawi Said 2018. More data can be found in the other submissions of the Tawi Said Archaeological Survey community. The site of Tawi Said is located in the Al-Sharqiyah governorate, approximately 5 km northwest of the modern city of Bidiyah, on the edge of the Sharqiyah Desert. It was discovered in 1976 by Beatrice de Cardi. Two years later, she returned to conduct small scale excavations at the site. Subsequently, numerous references to the site were made in the literature as the only known settlement of the Wadi Suq period (2000-1600 BC) in Central Oman. In November 2018, a short survey was conducted by the Goethe University Frankfurt, Germany, in Tawi Said. An area of 150 x 120 m was intensively field-walked in 1.5 m wide transects to ensure complete visual coverage of the investigated area. Each find received an ascending number and its exact location was recorded using a portable GPS device. In total, nearly 7500 objects were documented that date back to the Wadi Suq, as well as the (late) Islamic period. Among the finds, the largest group  of artefacts, by far, is of pottery sherds, followed by marine shells and snails, stone artefacts, metal objects, and jewellery. Furthermore, two stamp seals, one of them of a Wadi Suq period date, were found.

opencc-by-4.0Jun 2018View details →

UNIFI PRIN Florentine GIS data

UNIFI PRIN GIS data repository spatial data related to investigated area PRIN italian ADAPTIVE DESIGN e INNOVAZIONI TECNOLOGICHE PER LA RIGERNARAZIONE RESILIENTE DEI DISTRETTI URBANI IN REGIME DI CAMBIAMENTO CLIMATICO DIDA Dipartimento di Architetttura Università di Firenze This database was created to perform investigations for climatic urban resilience .