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Webis Open Directory Project Corpus 2010 (Webis-ODP-10)
<p>The Webis Open Directory Project Corpus 2010 (Webis-ODP-10) is a corpus for the evaluation of cluster labeling algorithms. The corpus contains about 5,000 web pages which are grouped into 4 main categories and 12 subcategories based on the human made <a href="http://www.dmoz.org/">Open Directory Project (ODP)</a> classification. The web pages were collected in May 2010.</p> <p>In order to create the corpus, 4 different categories of the Open Directory Project were chosen: movies, databases, health and recreation. Each of these categories is again consists of up to six subcategories. In the case of movies, these subcategories correspond to six different titles of two directors. The categories were chosen to allow for different settings of experiments. Example for tasks are to distinguish the two directors or to separate web pages about recreation from those about health.</p> <p>For each category, the linked web pages were downloaded if they still existed. Furthermore, web pages which are linked from these web pages were downloaded as well and considered to be in the same category. However, only web pages of a length of 1,000 or more characters (excluding HTML tags and scripts) were kept.</p>
A project-based learning approach for enhancing learning skills and motivation in software engineering
<p>Supplementary material for: <strong>A project-based learning approach for enhancing learning skills and motivation in software engineering</strong></p> <ul> <li><strong>DTSIPBLData.txt</strong>- Data set</li> <li><strong>ScriptRPBL.R:</strong> R Script tp perform the analysis of data.</li> <li><strong>Supplementary material- R Script.docx</strong>: Documented R Script</li> </ul>
Projecting boreal bird responses to climate change: the signal exceeds the noise
<p>Current and projected future potential boreal bird densities (4-km resolution)</p> <p>Citation for journal article associated with this dataset:<br> --------------------<br> Stralberg, D., S. M. Matsuoka, A. Hamann, E. M. Bayne, P. Sólymos, F. K. A. Schmiegelow, X. Wang, S. G. Cumming, and S. J. Song. 2015. Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25:52-69. http://dx.doi.org/10.1890/13-2289.1</p> <p>Coordinate System<br> ------------------<br> Projection: Lambert Conformal Conic<br> False Easting: 0.00000000<br> False Northing: 0.00000000<br> Central Meridian: -95.00000000<br> Standard Parallel 1: 49.00000000<br> Standard Parallel 2: 77.00000000<br> Latitude Of Origin: 0.00000000<br> Linear Unit: Meter<br> Datum: D WGS 1984</p> <p>Summary<br> -------<br> The boreal forest biome provides a resource-rich environment for breeding birds, supporting high species diversity and bird numbers. These birds are likely to shift their distributions northward in response to rapid climate change over the next century. We used a comprehensive dataset of avian point-count surveys from across boreal Canada and Alaska, combined with interpolated climate data, to develop bioclimatic niche models of current avian distribution and density for 80 boreal-breeding songbird species. We then used a downscaling of projected future climates to assess the potential for these species to change their distribution and abundance in response to climate change. Note that projections represent potential densities based on climatic conditions, land use and topography. They do not account for physiographic barriers such as the northern extent of the Rocky Mountains that may prevent colonization of otherwise suitable habitat. Therefore current species’ distributions may be over-estimated in certain regions, particularly in Alaska.</p> <p>Boosted regression tree models of species distribution were averaged across two sets of covariates (climate-only and climate + land use + topography), 11 bootstrap samples, and four global climate models. Mean projections and uncertainty estimates (coefficient of variation) are available for the current period (based on climate data from 1961-1990) and three future time periods (2011–2040, 2041­–2070, 2071–2100). Climate data layers available at tinyurl.com/ClimateNA.</p> <p>Contact<br> -------<br> Diana Stralberg, University of Alberta (stralber@ualberta.ca)<br> Boreal Avian Modelling Project (borealbirds.ca)</p> <p>Project sponsors<br> ----------------<br> Boreal Avian Modelling (BAM) Project<br> Alberta Biodiversity Management and Climate Change Adaptation Project</p> <p>Avian data providers<br> --------------<br> http://www.borealbirds.ca/index.php/data_partners<br> USGS Breeding Bird Survey<br> Breeding Bird Atlases of Canada</p> <p>BAM founding organisations and funders<br> --------------------------------------<br> Environment Canada<br> University of Alberta<br> Canadian BEACONs Project</p> <p>Financial supporters<br> --------------------<br> USFWS Neotropical Migratory Bird Conservation Act<br> Vanier Canada Graduate Scholarships</p> <p>Alberta Biodiversity Monitoring Institute<br> Alberta Innovates Technology Futures<br> Alberta Pacific Forest Industries Inc.<br> Climate Change and Emissions Management Corporation<br> Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring<br> Killam Trusts<br> Landscape Conservation Cooperatives<br> National Fish and Wildlife Foundation<br> Université Laval</p> <p>Species code definitions<br> ------------------------<br> Code Common name (Scientific name)<br> ALFL Alder Flycatcher (Empidonax alnorum) ‡<br> AMCR American Crow (Corvus brachyrhynchos)<br> AMGO American Goldfinch (Spinus tristis)<br> AMPI American Pipit (Anthus rubescens) ‡<br> AMRE American Redstart (Setophaga ruticilla)<br> AMRO American Robin (Turdus migratorius) ‡<br> ATSP American Tree Sparrow (Spizella arborea) ‡<br> BAWW Black-and-white Warbler (Mniotilta varia)<br> BBWA Bay-breasted Warbler (Setophaga castanea)<br> BCCH Black-capped Chickadee (Poecile atricapillus) ‡<br> BHCO Brown-headed Cowbird (Molothrus ater)<br> BHVI Blue-headed Vireo (Vireo solitarius)<br> BLBW Blackburnian Warbler (Setophaga fusca)<br> BLJA Blue Jay (Cyanocitta cristata)<br> BLPW Blackpoll Warbler (Setophaga striata) ‡<br> BOCH Boreal Chickadee (Poecile hudsonicus) ‡<br> BRBL Brewer’s Blackbird (Euphagus cyanocephalus)<br> BRCR Brown Creeper (Certhia americana) ‡<br> BTNW Black-throated Green Warbler (Setophaga virens)<br> CAWA Canada Warbler (Cardellina canadensis)<br> CCSP Clay-colored Sparrow (Spizella pallida)<br> CEDW Cedar Waxwing (Bombycilla cedrorum)<br> CHSP Chipping Sparrow (Spizella passerina) ‡<br> CMWA Cape May Warbler (Setophaga tigrina)<br> COGR Common Grackle (Quiscalus quiscula)<br> CONW Connecticut Warbler (Oporornis agilis)<br> CORA Common Raven (Corvus corax) ‡<br> CORE Common Redpoll (Acanthis flammea) ‡<br> COYE Common Yellowthroat (Geothlypis trichas)<br> CSWA Chestnut-sided Warbler (Setophaga pensylvanica)<br> DEJU Dark-eyed Junco (Junco hyemalis) ‡<br> EAKI Eastern Kingbird (Tyrannus tyrannus)<br> EAPH Eastern Phoebe (Sayornis phoebe)<br> EVGR Evening Grosbeak (Coccothraustes vespertinus)<br> FOSP Fox Sparrow (Passerella iliaca) ‡<br> GCKI Golden-crowned Kinglet (Regulus satrapa) ‡<br> GCTH Gray-cheeked Thrush (Catharus minimus) ‡<br> GRAJ Gray Jay (Perisoreus canadensis) ‡<br> HETH Hermit Thrush (Catharus guttatus) ‡<br> HOLA Horned Lark (Eremophila alpestris) ‡<br> LCSP Le Conte's Sparrow (Ammodramus leconteii)<br> LEFL Least Flycatcher (Empidonax minimus)<br> LISP Lincoln's Sparrow (Melospiza lincolnii) ‡<br> MAWA Magnolia Warbler (Setophaga magnolia)<br> MOWA Mourning Warbler (Geothlypis philadelphia)<br> NAWA Nashville Warbler (Oreothlypis ruficapilla)<br> NOWA Northern Waterthrush (Parkesia noveboracensis) ‡<br> OCWA Orange-crowned Warbler (Oreothlypis celata) ‡<br> OSFL Olive-sided Flycatcher (Contopus cooperi) ‡<br> OVEN Ovenbird (Seiurus aurocapilla)<br> PAWA Palm Warbler (Setophaga palmarum)<br> PHVI Philadelphia Vireo (Vireo philadelphicus)<br> PIGR Pine Grosbeak (Pinicola enucleator) ‡<br> PISI Pine Siskin (Spinus pinus) ‡<br> PUFI Purple Finch (Carpodacus purpureus)<br> RBGR Rose-breasted Grosbeak (Pheucticus ludovicianus)<br> RBNU Red-breasted Nuthatch (Sitta canadensis) ‡<br> RCKI Ruby-crowned Kinglet (Regulus calendula) ‡<br> REVI Red-eyed Vireo (Vireo olivaceus)<br> RUBL Red-winged Blackbird (Agelaius phoeniceus) ‡<br> RWBL Rusty Blackbird (Euphagus carolinus) ‡<br> SAVS Savannah Sparrow (Passerculus sandwichensis) ‡<br> SOSP Song Sparrow (Melospiza melodia)<br> SWSP Swamp Sparrow (Melospiza georgiana)<br> SWTH Swainson's Thrush (Catharus ustulatus) ‡<br> TEWA Tennessee Warbler (Oreothlypis peregrina)<br> TRES Tree Swallow (Tachycineta bicolor) ‡<br> VATH Varied Thrush (Ixoreus naevius) ‡<br> VESP Vesper Sparrow (Pooecetes gramineus)<br> WAVI Warbling Vireo (Vireo gilvus)<br> WCSP White-crowned Sparrow (Zonotrichia leucophrys) ‡<br> WETA Western Tanager (Piranga ludoviciana)<br> WEWP Western Wood-Pewee (Contopus sordidulus) ‡<br> WIWA Wilson's Warbler (Cardellina pusilla) ‡<br> WIWR Winter Wren (Troglodytes hiemalis)<br> WTSP White-throated Sparrow (Zonotrichia albicollis)<br> WWCR White-winged Crossbill (Loxia leucoptera) ‡<br> YBFL Yellow-bellied Flycatcher (Empidonax flaviventris)<br> YRWA Yellow-rumped Warbler (Setophaga coronata) ‡<br> YWAR Yellow Warbler (Setophaga petechia) ‡</p> <p>‡ symbols denote the 38 species currently breeding in the Alaskan boreal region.<br> </p>
Microclimate proxy measurements from a logging gradient in Malaysian Borneo (BALI project)
<b>Description: </b><p>Overview<br>We focused our empirical measurements in Sabah, Borneo. We measured a microclimate thermal proxy sensitive to radiative, convective, and conductive heat fluxes. This thermal proxy is relevant to a range of organismal processes including plant regeneration, animal behavior, and soil nutrient fluxes. Measurements were made across space and time within three 1-ha plots comprising a gradient from old growth to heavy selective logging. We then combined these data with nearby weather station air temperature data, as well as measurements of topography and canopy structure derived from detailed ground surveys and airborne LIDAR.<br>Sampling design<br>Study location<br>We identified three sites along a logging intensity gradient in the Malaysian state of Sabah in Borneo. Sites each contain a 1-ha plot in lowland mixed dipterocarp forest. The plots are chosen to contrast an unlogged old growth forest with a moderately logged forest and a heavily logged forest <br>Microclimate<br>To map the microclimate thermal proxy in each plot, we installed dataloggers on a semi-regular grid pattern varying in minimum distance from 1 to 14 meters. Each datalogger was a Thermochron iButton (DS1921G, Maxim), capable of logging up to 2048 temperature values between -30°C and 70°. Each datalogger was waterproofed by wrapping in plastic paraffin film (Parafilm, Bemis) and then in light yellow duct tape. Dataloggers were attached using plastic zip-ties onto PVC stakes at a height of 1-3 cm immediately above the forest floor. The tape color was chosen to approximate the albedo of vegetation/soil, and the size of the sensor package was chosen to have a similar boundary layer to many small organisms (e.g. tree seedlings, fallen branches, large insects). The sensor packages intentionally did not include a radiation shield, as the intent was not to measure air temperature. Temperatures recorded by the loggers therefore reflect a combination of conductive, convective, and radiative heat fluxes, and can be considered a rough proxy for those experienced by small organisms. <br>Each plot contains 25 20 × 20 m2 subplots, demarcated by 1 m-high PVC stakes embedded in the soil. Each subplot also contains at its center a mesh litter trap suspended on PVC stakes at 1-m height. Exact locations for all subplot corner and center stakes were determined using ground-based Field-Map software (IFER, Jílové u Prahy, Czech Republic). Spatial positions were recorded in three-dimensional space (local x, y, z-coordinates) using an Impulse 200 Standard laser rangefinder, MapStar Module II electronic compass (Laser Technology, Colorado, USA).<br>We installed a datalogger on these stakes at the corner of each subplot and the center of each subplot. We also chose at random three focal subplots in each plot for higher-resolution sampling. Within these subplots we established a cross-type design, with six additional dataloggers deployed at 1 to 5 m distance on additional PVC stakes located near each litter trap. A total of 239 dataloggers were installed.<br>Dataloggers were deployed during the end of the dry season in late 2015. Each recorded 28 days of data at 20-minute intervals. Start times were synchronized among dataloggers within plots. The exact date of deployment was 1 November for the heavily logged plot and 9 November for the moderately logged and old growth plot. Weather conditions during November-December 2015 were consistently dry and hot, so we do not anticipate any biases from the differing start dates. In nearly all cases dataloggers were recovered in their original location, except for a small number that were transported 1-2 meters down slopes. We treated data from these as though they were in their original position. A small number of dataloggers also failed due to being lost or punctured by animal bites. 90% of dataloggers (214/239) were successfully recovered and downloaded.<br>Air temperature data<br>To compare the microclimate thermal proxy to other temperature metrics, we obtained off-plot (open site) and on-plot (below canopy) weather station data. To represent off-plot data for both the moderately and heavily logged plots, a weather station was located in a cleared area at the SAFE base camp (4.724341°N, 117.601449°E), at a distance of 2.0 km from the heavily logged plot and 3.9 km from the moderately logged plot. Data were logged continuously (Datahog, Skye Instruments, UK). Measurements included air temperature (°C) and photosynthetically active radiation (W m-2). Data were available for all of the study period. To represent off-plot data for the old growth plot, another weather station was located in a cleared area at the Maliau Basin Studies Center (4.736263°N, 116.97662°E), at a distance of 1.4 km from the plot. Available data only included photosynthetically active radiation (W m-2). Data were available for approximately 25% of the study period. We predicted air temperature values at this plot for these dates by calibrating a LOESS regression model of air temperature based on time of day (seconds after midnight) and photosynthetically active radiation, calibrated with weather station data from an open clearing at the SAFE base camp (78 km distance, 184 m lower elevation). Because of the small elevation change we did not include a further lapse rate correction for temperature. The fitted model, which had a residual standard error of 0.9°C, was used to predict off-plot air temperature at the old growth plot.<br>To represent on-plot air temperature, we located air temperature sensors (HOBO, U23-002) within radiation shields at 1.5 m height in a subplot within each plot (corresponding to a focal subplot with a higher density of microclimate dataloggers: old growth, subplot 18; moderately logged, subplot 24; heavily logged, subplot 25). Temperature was measured hourly.<br>LiDAR data<br>Discrete airborne LiDAR data were acquired by NERC's Airborne Research Facility (ARF) in November of 2014 using a Leica ALS50-II LiDAR sensor flown on a Dornier 228-20 at 41 points m-2 density, with up to four returns recorded per pulse. Georeferencing of the point cloud was ensured by incorporating data from a Leica base station in the study area. LiDAR point clouds were classified into ground and non-ground points, and used to produce a 1 m resolution canopy height model by averaging the first returns. Gaps in the canopy height model were filled by averaging neighboring cells.<br>Topography<br>The ground-mapped coordinates of the subplot corners, subplot centers, and all stems were used to construct a digital elevation model (DEM) for the plot. Elevation was interpolated onto a 1 m grid using ordinary kriging with a minimum of 4 points and search radius of 30 meters. This grid was then aligned to the LIDAR-determined location and elevation of the plot corners. The DEM was then used to estimate slope (in degrees) and cosine of aspect (with higher values indicating more southerly exposures) for each location.<br>Forest structure<br>Forest structure was determined from field surveys and from airborne laser scanning. For the field survey, all trees ≥10 cm diameter at 1.3 m height were censused in each plot in 2016. Diameter was measured at 1.3 m with a tape measure, height with laser rangefinders, and x-y position of each stem were determined using the same system as the subplot corners. The horizontal crown projection of every tree was mapped by measuring spatial positions (x and y-coordinates) of 5 to 30 points (depending on the size of the crown) at the boundary of a crown projected to the horizontal plane and then smoothed using Field-Map software. <br>Field stem maps were then converted into raster grids of stem basal area density (smoothed with 2-meter Gaussian kernel, and then rasterized to 1 m resolution), canopy density (number of overlying canopies per unit area) (1 m resolution), and plant area index (PAI) (10 m native resolution, interpolated to 1 m resolution). Spatial variation in PAI was mapped from the LiDAR point cloud using the MacArthur-Horn method. The method assumes that the leaves are randomly distributed within the laterally homogeneous canopy layers, so the PAI is proportional to the logarithm of the fraction of LiDAR pulses, β, penetrating through the canopy: PAI = -1/κ ln(β), where κ is a correction factor that accounts for canopy features, such as clumping and the distribution of leaf angles. We assumed a constant value of κ=0.7. Only the first returns, representing the first interaction of each LiDAR pulse with the canopy, are considered. We employed a lower cutoff of 2 m to avoid confusing ground returns with low-lying vegetation. PAI was estimated for point locations along a 1 m regular grid using circular sampling neighborhood of 10 m. This sampling window size is used to capture a sufficient number of LiDAR returns to avoid saturation effects in the more densely vegetated parts of the plots. This approach for calculating canopy closure may be biased, as clumping of vegetation, variation in leaf angle, and canopy edges (i.e. at gaps) should lead to spatial variation in the κ coefficient. It was not possible with our data to constrain κ using hemispherical photos due to saturation effects.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/22"><b>Drivers of microclimate variation in disturbed forests</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC independent research fellowship (Standard grant, NE/M019160/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000/2/2(JLD.3((126) )</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266822">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Blonder_Mircoclimate.xlsx, Mircoclimate_proxy.zip</p><p><b>Blonder_Mircoclimate.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mircoclimate datalogger data</b> (described in worksheet Data_Sheet_S1)</p><p>Description: Raw temperature measurements for all microclimate dataloggers</p><p>Number of fields: 7</p><p>Number of data rows: 444416</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Tag</b>: The field code for the data logger (Field type: ID)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Forest.floor.temperature..degC.</b>: Microclimate proxy reading (Field type: Numeric)</li><li><b>X..m.</b>: Location of the data logger in meters east in UTM zone 50N (Field type: Numeric)</li><li><b>Y..m.</b>: Location of the data logger in meters north in UTM zone 50N (Field type: Numeric)</li><li><b>Z..m.</b>: Location of the data logger in meters above sea level (Field type: Numeric)</li></ul></li><li><p><b>Weather station data</b> (described in worksheet Data_Sheet_S3)</p><p>Description: Raw off-plot and on-plot weather station air temperature data</p><p>Number of fields: 4</p><p>Number of data rows: 1397</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Off.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the off-plot weather station (Field type: Numeric)</li><li><b>On.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the on-plot weather station (Field type: Numeric)</li></ul></li></ol><p><b>Mircoclimate_proxy.zip</b></p><p>Description: S2 Spatial datasets for microclimate, topography, and canopy structure. All datasets are spatially interpolated to 1m resolution and projected into UTM coordinates, zone 50N. </p><p><b>Date range: </b>2015-11-01 to 2015-12-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
PERCEIVE: WP2: Evaluation of EU citizens' perception of the EU project: Task2.1: Data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables</p> <p>2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)</p> <p>3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)</p> <p>4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)</p> <p>5. codebook for all regional data</p>
Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron
<p>This is the data set for Zhang et al. 2019 "Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron" published at eLife.</p>
UKRR Project Data Master List
<p>This is the main data output of the UKRR project. The data was extracted using the LARCH database on 29th March 2019. It includes everything submitted by all libraries during Phases 2 and 3, but only some data from Phase 1. Phase 1 did not use the LARCH system, and <em>some </em>of the Phase 1 submissions were retrospectively added, but others were not. As far as records show, of the Phase 1 contributors, this dataset includes: </p> <p>All the Phase 1 contributions from Liverpool, Newcastle and St Andrews.</p> <p>Some of the Phase 1 contributions from Birmingham and Southampton. </p> <p>None of the data from Cardiff, Exeter and Imperial.</p> <p>The dataset includes information on journal title changes and cataloguing inconsistencies (all of which are listed in column B), grouping them together and assigning a BL Overlap number (column A), and giving all relevant International Standard Serial Numbers (ISSNs) in Column C. Column D is the number of different titles under which a specific journal was offered. Column E shows the number of times material from that journal was offered. Column F shows the libraries that offered the material, as well as the number of times each library offered it. </p> <p>All UKRR participant libraries agreed to release the data without anonymisation in the interests of openness and to allow greater scope for future analysis. </p>
Gaze-Stabilizing Central Vestibular Neurons Project Asymmetrically to Extraocular Motoneuron Pools
<p><strong>ABSTRACT </strong>Within reflex circuits, specific anatomical projections allow central neurons to relay sensations to effectors that generate movements. A major challenge is to relate anatomical features of central neural populations, such as asymmetric connectivity, to the computations the populations perform. To address this problem, we mapped the anatomy, modeled the function, and discovered a new behavioral role for a genetically defined population of central vestibular neurons in rhombomeres 5–7 of larval zebrafish. First, we found that neurons within this central population project preferentially to motoneurons that move the eyes downward. Concordantly, when the entire population of asymmetrically projecting neurons was stimulated collectively, only downward eye rotations were observed, demonstrating a functional correlate of the anatomical bias. When these neurons are ablated, fish failed to rotate their eyes following either nose-up or nose-down body tilts. This asymmetrically projecting central population thus participates in both upward and downward gaze stabilization. In addition to projecting to motoneurons, central vestibular neurons also receive direct sensory input from peripheral afferents. To infer whether asymmetric projections can facilitate sensory encoding or motor output, we modeled differentially projecting sets of central vestibular neurons. Whereas motor command strength was independent of projection allocation, asymmetric projections enabled more accurate representation of nose-up stimuli. The model shows how asymmetric connectivity could enhance the representation of imbalance during nose-up postures while preserving gaze stabilization performance. Finally, we found that central vestibular neurons were necessary for a vital behavior requiring maintenance of a nose-up posture: swim bladder inflation. These observations suggest that asymmetric connectivity in the vestibular system facilitates representation of ethologically relevant stimuli without compromising reflexive behavior.</p> <p><strong>SIGNIFICANCE STATEMENT</strong> Interneuron populations use specific anatomical projections to transform sensations into reflexive actions. Here we examined how the anatomical composition of a genetically defined population of balance interneurons in the larval zebrafish relates to the computations it performs. First, we found that the population of interneurons that stabilize gaze preferentially project to motoneurons that move the eyes downward. Next, we discovered through modeling that such projection patterns can enhance the encoding of nose-up sensations without compromising gaze stabilization. Finally, we found that loss of these interneurons impairs a vital behavior, swim bladder inflation, that relies on maintaining a nose-up posture. These observations suggest that anatomical specialization permits neural circuits to represent relevant features of the environment without compromising behavior.</p>
Example data from radio-tracking.eu project
<p>Example of radio tracking data of a tagged bechsteins bat recorded in marburg open forest as part of the LOEWE priority project Nature 4.0 – Sensing Biodiversity funded by the Hessen State Ministry for Higher Education, Research and the Arts, Germany.</p>
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
Underlying data of the project "Are CONSORT checklists submitted by authors adequately reflecting what information is actually reported in published papers?"
<p><em><strong>Supplementary file 3.xlsx</strong></em> contains the evaluations for the 12 papers included in the study.</p>
Speadsheet data cited in Annex 2 of Deliverable 4.1 of project ASTRail
<p>This repository contains the spreadsheet for the "Ranking Matrix" computations,<br> as mentioned in the Annex 2 of Deliverable 4.1 "Report on Analysis and on Ranking of Formal Methods"<br> of the project ASTRail (http://www.astrail.eu).</p>
Water discharge rates at SAFE Project experimental streams
<b>Description: </b><p>Data for calculating stream discharge in SAFE's experimental rivers</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/115"><b>Effects of retaining riparian forest zones of differing width on stream hydrology following conversion to oil palm</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3479171">here</a></p><p><b>Files: </b>This consists of 1 file: Template_hydro.xlsx</p><p><b>Template_hydro.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Stream level data for estimating discharge</b> (described in worksheet StreamLevels)</p><p>Description: Five-minute recordings of stream height Each recording has 10 separate values</p><p>Number of fields: 14</p><p>Number of data rows: 141659</p><p>Fields: </p><ul><li><b>Site</b>: Site of datalogger (Field type: location)</li><li><b>Datetime</b>: Date and time of measurement (Field type: datetime)</li><li><b>Distance1</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance2</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance3</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance4</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance5</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance6</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance7</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance8</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance9</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance10</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>LoggerBattery</b>: Battery level of the datalogger; max = 4.20V (Field type: numeric)</li><li><b>LoggerTemp</b>: Temperature of the datalogger (Field type: numeric)</li></ul></li><li><p><b>Salt dilution experiments to estimate discharge rates</b> (described in worksheet SaltDilution)</p><p>Description: Salt dilution experiments to estimate discharge rates. Experiments were repeated at each stream on multiple dates. Salt was added 2.5 minutes into each experiment. Details about sites and conditions relating to the experiments are in the 'SaltDilutionMeta' worksheet.</p><p>Number of fields: 6</p><p>Number of data rows: 14620</p><p>Fields: </p><ul><li><b>Site</b>: SAFE stream where dilution was carried out (Field type: location)</li><li><b>Date</b>: Date of dilution experiment (Field type: date)</li><li><b>Treatment</b>: Was salt added or not? (Field type: categorical)</li><li><b>Time</b>: Time since measurements began (Field type: numeric)</li><li><b>Conductivity</b>: Conductivity (Field type: numeric)</li><li><b>SaltConc</b>: Salt concentration (Field type: numeric)</li></ul></li><li><p><b>Calibration for salt dilution experiment</b> (described in worksheet SaltDilutionCalib)</p><p>Description: Calibrating salt concentration to conductivity measuments for salt dilution gauging</p><p>Number of fields: 3</p><p>Number of data rows: 8</p><p>Fields: </p><ul><li><b>Concentration</b>: Salt concentration (Field type: numeric)</li><li><b>Conductivity</b>: Conductivity (Field type: numeric)</li><li><b>Temp</b>: Water temperature (Field type: numeric)</li></ul></li><li><p><b>Salt dilution experiment metadata</b> (described in worksheet SaltDilutionMeta)</p><p>Description: Metadata giving context for salt dilution experiment data in 'SaltDilution' worksheet</p><p>Number of fields: 16</p><p>Number of data rows: 23</p><p>Fields: </p><ul><li><b>Site</b>: Stream on which salt dilution took place (Field type: location)</li><li><b>Date</b>: Date experiment was conducted (Field type: date)</li><li><b>Treatment</b>: Was salt added or not? (Field type: categorical)</li><li><b>SaltAmount</b>: Amount of salt added (Field type: numeric)</li><li><b>Start</b>: Start time (Field type: time)</li><li><b>End</b>: End time (Field type: time)</li><li><b>InitConducRelease</b>: Conductivity at start of experiment at location where salt was poured in (Field type: numeric)</li><li><b>InitConducRecord</b>: Conductivity at start of experiment at location where measurements were recorded (Field type: numeric)</li><li><b>EndConducRelease</b>: Conductivity at end of experiment at location where salt was poured in (Field type: numeric)</li><li><b>EndConducRecord</b>: Conductivity at end of experiment at location where measurements were recorded (Field type: numeric)</li><li><b>ReleaseLat</b>: Latitude of location where salt was released (Field type: numeric)</li><li><b>ReleaseLong</b>: Longitude of location where salt was released (Field type: numeric)</li><li><b>RecordLat</b>: Latitude of location where conductivity was recorded (Field type: numeric)</li><li><b>RecordLong</b>: Longitude of location where conductivity was recorded (Field type: numeric)</li><li><b>Distance between poured and measured (m)</b>: Distance between location where salt was released and conductivity was recorded (Field type: numeric)</li><li><b>Video</b>: Link to video of conductivity metre (Field type: id)</li></ul></li></ol><p><b>Date range: </b>2019-01-24 to 2019-07-01</p><p><b>Latitudinal extent: </b>4.6498 to 4.7317</p><p><b>Longitudinal extent: </b>117.5413 to 117.6233</p>
Mammal trapping grid data for the SAFE Project
<b>Description: </b><p>This zipfile contains point grids used for mammal live trapping and camera trapping at the SAFE Project. There are 46 grids, each one consisting of a 4 by 12 grid of points, spread across blocks of old growth forest, logged forest and oil palm.<br><br>The grids were established by Ollie Wearn and processed by David Orme to convert the original GIS files to a single dataset using the UTM 50N projetion. Details of the geoprocessing can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/mammaltraps">https://www.safeproject.net/dokuwiki/safe_gis/mammaltraps</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3490613">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_Mammal_traps_metadata.xlsx, SAFE_MammalTraps_UTM50N_WGS84.zip</p><p><b>SAFE_Mammal_traps_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SAFE_MammalTraps_UTM50N_WGS84.zip</b></p><p>Description: Shapefile containing 2208 point locations of trapping grid points across SAFE locations</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet Properties)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 8</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>GPS_Ident</b>: Name of trap point in original GPS ident (Field type: location)</li><li><b>Altitude</b>: GPS Elevation of point (Field type: numeric)</li><li><b>Project</b>: Project name - all values are 'SAFE' (Field type: categorical)</li><li><b>Site</b>: SAFE Project main block identity for point (Field type: categorical)</li><li><b>Station</b>: Identical to GPS_Ident (Field type: location)</li><li><b>Location</b>: Specific location within main block (Field type: categorical)</li><li><b>Block</b>: Sub-block number or location within main site block (Field type: categorical)</li><li><b>Number</b>: Trap point number - each grid has 48 points (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.6348 to 4.7539</p><p><b>Longitudinal extent: </b>116.9471 to 117.6254</p>
Country borders and shorelines around the SAFE Project
<b>Description: </b><p>This dataset contains regional country boundaries around the SAFE project from two online sources.<br><br><b>Digital chart of the world</b> from <a href="https://worldmap.harvard.edu/data/geonode:Digital_Chart_of_the_World">https://worldmap.harvard.edu/data/geonode:Digital_Chart_of_the_World</a>.<br><br><b>A Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG)</b>. This is a large vector GIS database containing shorelines for seas and lakes and other vector data layers at a number of different resolutions. The full archive was downloaded from: < href='http://www.soest.hawaii.edu/pwessel/gshhg/'>http://www.soest.hawaii.edu/pwessel/gshhg/<br><br>Details of the file extraction and coverage can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/country_shoreline">https://www.safeproject.net/dokuwiki/safe_gis/country_shoreline</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492126">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_Country_border_metadata.xlsx, SAFE_shore_and_country_borders.zip</p><p><b>SAFE_Country_border_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SAFE_shore_and_country_borders.zip</b></p><p>Description: Contains three shapefiles of local country borders</p><p>This file contains 3 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet SAFE_Regional_DCW)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 13</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>ID</b>: Feature ID (Field type: id)</li><li><b>FIPS10_4</b>: FIPS country code (Field type: categorical)</li><li><b>COUNTRY</b>: Country name (Field type: categorical)</li><li><b>CONTCODE</b>: Continent code (Field type: categorical)</li><li><b>CONT</b>: Continent name (Field type: categorical)</li><li><b>F_CODE</b>: Geo code (Field type: categorical)</li><li><b>F_CODE_DES</b>: Description of F code (Field type: comments)</li><li><b>OW_ABBREV</b>: OW country codes (Field type: categorical)</li><li><b>ISO_A2</b>: Two letter ISO country code (Field type: categorical)</li><li><b>ISO_A3</b>: Three letter ISO country code (Field type: categorical)</li><li><b>VMAP_ID</b>: Base vector map source ids (Field type: categorical)</li><li><b>FACTBK_CTY</b>: Factbook Country name (Field type: categorical)</li><li><b>Fips2</b>: FIPS 2 character code (Field type: categorical)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet SAFE_Regional_GSHHS_Shoreline_f)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 6</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>id</b>: Feature ID (Field type: id)</li><li><b>level</b>: Level of nesting of feature (Field type: numeric)</li><li><b>source</b>: Source of original geodata (Field type: categorical)</li><li><b>parent_id</b>: Unknown feature ID reference (all values = -1) (Field type: id)</li><li><b>sibling_id</b>: Unknown feature ID reference (all values = -1) (Field type: id)</li><li><b>area</b>: Area of feature (Field type: numeric)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet SAFE_Regional_GSHHS_Shoreline_i)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 6</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>id</b>: Feature ID (Field type: id)</li><li><b>level</b>: Level of nesting of feature (Field type: numeric)</li><li><b>source</b>: Source of original geodata (Field type: categorical)</li><li><b>parent_id</b>: Unknown feature ID reference (all values = -1) (Field type: id)</li><li><b>sibling_id</b>: Unknown feature ID reference (all values = -1) (Field type: id)</li><li><b>area</b>: Area of feature (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>-11.0049 to 21.1200</p><p><b>Longitudinal extent: </b>95.0000 to 141.0200</p>
Stream network for the SAFE Project derived from SRTM data
<b>Description: </b><p>This zipfile contains linear geometries showing a channel network across the SAFE Project landscape. The network was calculated using the GRASS hydrology tool <code>r.stream.extract</code> from SRTM elevation data and the resulting flow accumulation predictions (see <a href="https://zenodo.org/record/3490488">https://zenodo.org/record/3490488</a> and <a href="https://zenodo.org/record/3490687">https://zenodo.org/record/3490687</a>).<br><br>Note that these networks are derived entirely from remotely sensed data, but do form a single interconnected network across the wider SAFE landscape. Other stream network data are available () but the provenance of these are not well known and they only cover part of the SAFE network.<br><br>Details of the geoprocessing can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/stream_networks">https://www.safeproject.net/dokuwiki/safe_gis/stream_networks</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492009">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_SRTM_Stream_network_metadata.xlsx, SRTM_Channels_network.zip</p><p><b>SAFE_SRTM_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Channels_network.zip</b></p><p>Description: Shapefile containing 54413 calculated segments forming a channel network across the wider SAFE landscape.</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet Properties)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 17</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>cat</b>: Identity of segment (Field type: id)</li><li><b>type_cd</b>: Segment type (see also type) (Field type: numeric)</li><li><b>ID</b>: Identity of segment (Field type: id)</li><li><b>length</b>: Length of network segment (Field type: numeric)</li><li><b>n_ponts</b>: Number of points in segment (Field type: numeric)</li><li><b>sourceX</b>: X coordinate of source point of segment (Field type: numeric)</li><li><b>sourceY</b>: Y coordinate of source point of segment (Field type: numeric)</li><li><b>sinkX</b>: X coordinate of sink point of segment (Field type: numeric)</li><li><b>sinkY</b>: Y coordinate of sink point of segment (Field type: numeric)</li><li><b>type</b>: Segment type - is the segment a source or outflow segment (connected at one end only, type_cd = 0) or internal (connected at both ends, type_cd=1) (Field type: categorical)</li><li><b>snkChnn</b>: Identity of sink channel for this segment (Field type: id)</li><li><b>srcChnn</b>: Identities of source channels for this segment (Field type: id)</li><li><b>nSourcs</b>: Count of source channels (Field type: numeric)</li><li><b>sorcElv</b>: Elevation of start point of segment (Field type: numeric)</li><li><b>sinkElv</b>: Elevation of sink point of segment (Field type: numeric)</li><li><b>sorcFlw</b>: Incoming flow at segment source (Field type: numeric)</li><li><b>sinkFlw</b>: Outgoing flow at segment sink (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Alternative stream network data for the SAFE Project
<b>Description: </b><p>This zipfile contains three shapefiles of linear geometries showing channel networks across the SAFE landscape.<br></p><ul><br><li>RIVERS_UTM.shp: From Igor Lysenko's SAFE pack, this is a river network with two areas, covering Maliau and the area around the SAFE experimental region. The network density is much higher in Maliau than SAFE and the channel network around SAFE is not complete. This is projected in UTM50N WGS84.</li><br><li>LFEriver_SL.shp: This is provided (by Sarah Luke?) through Clare Wilkinson's GIS files and adds a critical missing stream for the Logged Forest Edge (LFE) catchment. This is projected in UTM50N WGS84.</li><br><li>all_rivers.shp: This is provided through Clare Wilkinson's GIS files and provides streams for a single region covering Danum and the SAFE experimental region but not sampled watersheds in Oil Palm plantations to the south of SAFE. The original file is missing projection information (no .prj file) but other files in the same dataset are projected in RSO Timbalai 1948 and using this projection fits with the context of other data. The version uploaded onto Zenodo has been reprojected into UTM50N WGS84.</li><br></ul><br><br>Although the files are not consistent, they do contain channels and channel data that are referenced in some studies. The provenance of these files are unknown, although the following suggests that they may be traced using GPS or from imagery:<br><ul><br><li>Incomplete coverage within the regions they cover.</li><br><li>Treatment of larger rivers, changing from a single line feature showing the stream centreline (?) to double lines showing the river banks.</li><br><li>Variation in network density: the western edge of all_rivers.shp shows a vertical band about 4 km wide of higher stream density than the rest of the region; RIVERS_UTM.shp shows marked differences in stream density between SAFE and Maliau.</li><br></ul><p></p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492064">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_Alternative_Stream_network_metadata.xlsx, Preexisting_SAFE_river_files.zip</p><p><b>SAFE_Alternative_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>Preexisting_SAFE_river_files.zip</b></p><p>Description: Contains three shapefiles of channel networks.</p><p>This file contains 3 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet LFEriver_SL)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>Id</b>: Identity of river (Field type: id)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet RIVERS_UTM)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>NAME</b>: Local name of segment (Field type: comments)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet all_river_UTM50N_WGS84)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 10</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>FNODE_</b>: Unknown (Field type: numeric)</li><li><b>TNODE_</b>: Unknown (Field type: numeric)</li><li><b>LPOLY_</b>: Unknown (Field type: numeric)</li><li><b>RPOLY_</b>: Unknown (Field type: numeric)</li><li><b>LENGTH_MET</b>: Length of channel segment in metres (Field type: numeric)</li><li><b>RIV_YSC_</b>: Unknown (Field type: numeric)</li><li><b>RIV_YSC_ID</b>: Unknown (Field type: numeric)</li><li><b>CODE</b>: Unknown (Field type: numeric)</li><li><b>NAME</b>: Local name of segment (Field type: comments)</li><li><b>AREA</b>: Local area of segment (Field type: comments)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Improving quality of life among healthy individuals through theatre activities: a theatre-in-health pilot project
<p><strong>Background</strong></p> <p>Review of previous studies regarding arts and health reveals knowledge gaps regarding the use of theatre activities outside a clinical setting with healthy participants. The purpose of this study was to evaluate quality of life benefits of structured theatre training improvisations among generally healthy individuals.</p> <p><strong>Methods</strong></p> <p>A mixed methods approach was used at three theatre activities workshops. Pre- and post-quality of life surveys were completed at administered at each workshop. workshop. Participants also free wrote after the workshops, reflecting on their experiences.</p> <p><strong>Results</strong></p> <p>The surveys showed that there was a statistically significant shift in a positive direction in each domain studied. Themes that emerged from the free-writing responses were those of play, social acceptance, gratitude, and community.</p> <p><strong>Conclusions</strong></p> <p>Findings demonstrate that structured theatre activities produce quality of life benefits across all domains. This pilot study indicates the potential for such theatre-in-health projects as a tool for quality of life improvement.</p>
Dataset of the Volkswagen Fond Project no. 90 216 " Early mounted nomads and their vessels Ceramic analysis project aimed at supporting the reconstruction of socio-economic conditions in mobile populations north of the Black Sea between 1100 and 600 BC"
<p>The Excel-Sheet contains information (location of the site, chronological position, type of site, archaeological culture, literature, photographies and drawings, analysis made, results of the analysis, etc.) about the samples taken in the Volkswagen Fond Project no. 90 216 "Early mounted nomads and their vessels Ceramic analysis project aimed at supporting the reconstruction of socio-economic conditions in mobile populations north of the Black Sea between 1100 and 600 BC".</p> <p>The project itself focuses on contacts and interactions between groups with different material culture remains in two vegetation zones to the north of the Black Sea.The investigation takes a complex archaeometric approach to characterise the pottery of communities that lived in the forest steppe zone and steppe zone to the north of the Black Sea between the Dniester and Dnieper rivers between 1100 and 600 BC. By using an multidisciplinary approach to study the material culture of these communities with mobile lifeways, this project will allow the comparison of the natural science and archaeological data for the first time.</p> <p>The Excel-Sheet presents the results of various analyses performed during the project and serves as the database for publications of the project members.</p>
R-Script surface contour: Republic of Moldova project report Free University Berlin
<p>This R-Script supplements (surface contour) the project of the Freie Universität Berlin with the title: <strong>Anwendung geoarchäoogischer Methoden zum Verständnis anthropogener Spuren der Poineşti-Lucaşeuca Kultur in Ivancea-Sub Pădure, Republik Moldau. </strong>The project was carried out by students during the module Projekt I at the Freien Universität Berlin. Fieldwork was conducted 16th - 23rd September 2018in the vicinity of Ivancea (central Moldova), about 30km north of the capital Chisinau. </p>
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