As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial meltwater stream attributes of the region. Optical topographic surveys were performed to produce a layout of the area studied. This file provides a list of distances and height differences for features along the transect line at each site ("T-points"). All heights are relative to the assumed zero value at the transect RM #1. Distances are horizontal.

This dataset is about: Scarp scan using terrestrial LiDAR at Ierapetra Fault, relative position, raw data. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.833353 for more information.

Population and Housing Censuses: Population order by Autonomous Community and censal population of 2011 and relative position in 2011, 2001, 1991 and 1981. Autonomous Community.

No description is available. Visit https://dataone.org/datasets/8fb23e5ca2939457514a8b96a52401c3 for complete metadata about this dataset.

Walking through an environment generates retinal motion, which humans rely on to perform a variety of visual tasks. Retinal motion patterns are determined by an interconnected set of factors, including gaze location, gaze stabilization, the structure of the environment, and the walker’s goals. The characteristics of these motion signals have important consequences for neural organization and behavior. However, to date, there are no empirical in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. Here, we collect measurements of the eyes, the body, and the 3D environment during locomotion. We describe properties of the resulting retinal motion patterns. We explain how these patterns are shaped by gaze location in the world, as well as by behavior, and how they may provide a template for the way motion sensitivity and receptive field properties vary across the visual field. Methods Raw data (not included in this dataset):

Pupil Labs Core eye tracker (World facing camera RGB video, infrared eye camera video) Shadow Motion Capture System (IMU based motion capture data, sensor orientation, and relative position data)

Processed (included if indicated):

Eye tracker data processed with Pupil Capture software provides world facing camera relative to 3D gaze vectors World-facing video processed with Meshroom to provide camera position and orientation, and 3D mesh terrain reconstruction Custom MATLAB code used to align 3D gaze vector aligned to estimated camera position, provides approximated eyeball center, eye direction, relative to 3D mesh (gaze data included in dataset) Custom Python code used with Blender to compute eye perspective depth images Custom MATLAB is used to approximate retinal motion given depth image + eye translation + eye rotation measurement. (Retinal motion histograms included)

A slope position classification based on the Harden-Young 10m DEM, created to support soil property prediction in the GRDC project Methods to predict plant available water capacity (PAWC) Lineage: The classification is based on a ranking of elevations relative to their surroundings in 70 m and 200 m radius windows. See metadata for further details.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThe dataset covers the following tree canopy categories:Environmental Justice Priority AreasCensus tracts composite / quintileExisting tree canopy percentage & environmental justice priority levelExisting tree canopyPossible tree canopyRelative percentage changeFor more information, please see the 2021 Tree Canopy Assessment.

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

This data set presents a collection of measurements carried out at CEHINAV Towing Tank. The colletion includes several rar files described below:

• Towing_test.rar: Raw data (ASCII files) containing the following information:
  • Time (Chanel 1)
  • Velocity (Chanel 2)
  • Resistance (Chanel 4)
  • Laser position bow (Chanel 5)
  • Laser position stern (Chanel 6)
  • Laser position longitudinal (Chanel 7)
  • Absolute carriage position (Chanel 8)
• Towed_self_propulsion: Raw data (ASCII files) containing the followig information:
  • Time (Chanel 1)
  • Velocity (Chanel 2)
  • RPM engine (Chanel 3)
  • Resistance (Chanel 4)
  • Laser position bow (Chanel 5)
  • Laser position stern (Chanel 6)
  • Laser position longitudinal (Chanel 7)
  • Thrust (Chanel 8)
  • Torque (Chanel 9)
  • Absolute carriage position (Chanel 11)
• Figures_coverage: PNG files with images of block coverage tested and files containing the relative position during the test.
• HESPERIDES_model.msd: Maxurf file with the geometry of the model tested.

The tests were conducted in february and november of 2020.

Chr: chromosome. Probe1: Upstream internal probe ID. Probe2: Downstream internal probe ID. Region: probeset that probe pair belong to. Cell_Type: Cell type. Count: Number of spot pairs analyzed. 100nm: Number of spot pairs within 100 nm. 350nm: Number of spot pairs within 350 nm. 1um: Number of spot pairs within 1 micron. Median: Median distance between spots. SD: standard deviation in distance between spots. Mean: mean distance between spots. CoV: Coefficient of variation in distance between spots. Pearson_spotvspot: PCC for correlation between radial position at upstream spot and radial position at downstream spot. Spearman_spotvspot: SCC for correlation between radial position at upstream spot and radial position at downstream spot. Pearson_r1vdist: PCC for correlation between radial position at upstream spot and distance between spots. Spearman_r1vdist: SCC for correlation between radial position at upstream spot and distance between spots. Pearson_r2vdist: PCC for correlation between radial position at downstream spot and distance between spots. Spearman_r2vdist: SCC for correlation between radial position at downstream spot and distance between spots. Slope_r1vdist: slope of linear model between radial position at upstream spot and distance between spots. Slope_r2vdist: slope of linear model between radial position at downstream spot and distance between spots. ANOVA_r1vdist: p-value of ANOVA test comparing spatial distance between spots by radial bin of upstream spot. ANOVA_r2vdist: p-value of ANOVA test comparing spatial distance between spots by radial bin of downstream spot. (CSV)

The relative unit labour cost (ULC) series measures the trading position of an individual country relative to its partners in the euro area and as such offers an indication about changes in its competitive position. The measure takes into account not only changes in market exchange rates, but also variations in relative price levels based on the unit labour cost and therefore can be used as indicators of competitiveness. The data are expressed as 10 years % change, and 1 year % change. A decrease in the relative unit labour cost index is regarded as an improvement of a country's competitive position relative to their trading partners in the euro area. Data source: Directorate General for Economic and Financial Affairs (DG ECFIN).

The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

The Atlas of Canada National Scale Data 1:1,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas large scale (1:1,000,000 to 1:4,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

Time series of cod age composition in bi-annual catches from fixed periods (spawning season and autumn) with fixed gear, of fixed duration and at fixed positions in the Trondheimsfjord. Routine data collecting (for database) started in 1973, but relative yearclass strength is calculated back to 1963. The age data are processed until 1999, and the relative yearclass strengths estimates until 1997.

Topographic Position Index (TPI) is a topographic position classification identifying upper, middle and lower parts of the landscape. This dataset includes a mask that identifies where topographic position cannot be reliably derived in low relief areas.

The TPI product was derived from Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. A masked version of the TPI product was derived using the slope relief classification product.

The TPI data are available at 1 arc-second and 3 arc-second resolution.

The 3 arc-second resolution dataset was generated from the 1 arc-second TPI product and masked by the 3” water and ocean mask datasets.

Lineage: Source data 1. 1 arc-second SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). 2. 1 arc-second slope relief product 3. 3 arc-second resolution SRTM water body and ocean mask datasets.

Topographic position index calculation TPI is a measure of topographic position, classified into three classes corresponding to upper slopes, mid-slopes and lower slopes. The method follows that of the "Drainage Channels Class" section of Warner, Cress and Sayre (2008) which is based on the TPI method of Jenness (2006) and Weiss (2001).

The TPI classification uses relative elevation as a fraction of local relief; where the relative elevation is high compared to the local relief the class is upper slope, and where the relative elevation is low compared to local relief the class is lower slope. Intermediate values are classified as mid-slopes. This use of residuals compared to a smoothed elevation model to produce relative elevations is similar to the method described by McRae (1992).

Relative elevation is the difference between local (cell) elevation and the mean elevation over a 300 m radius circle (approximately: the calculation actually uses 10 grid cells at 1 arc-second resolution). Local relief is calculated as the standard deviation of elevation over the same circular region. The classification is:

TPI = 1 if relative_elevation < -0.5 * local relief (lower slopes) 3 if relative_elevation > 0.5 * local relief (upper slopes) 2 otherwise (mid slopes)

In relatively flat areas the finite accuracy of a DEM limits its ability to discriminate topographic position. The mask included with the TPI layer identifies areas that are too flat to reliably identify upper, middle and lower landscape positions. It is based on the 'Slope-Relief' classification and the TPI mask has values of 1 where there is sufficient relief for TPI to be meaningful and 0 where TPI should not be used.

The TPI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.

The 3” arc-resolution version was generated from the 1” TPI class and mask products. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The result was then converted to integer format, avoiding truncation errors and ensuring that (for example) values between 1.5 and 2 were assigned to class 2, and values between 2.5 and 3 were assigned to class 3. The 3” TPI and TPI mask data were then masked using the SRTM 3” ocean and water body datasets.

The Atlas of Canada National Scale Data 1:15,000,000 Series consists of boundary, coast and coastal islands, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas small scale (1:15,000,000 and 1:30,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

The number of position emission tomography scanners in Austria increased by one scanner (+4.35 percent) in 2021 in comparison to the previous year. Therefore, the number of position emission tomography scanners in Austria reached a peak in 2021 with 24 scanners. Positron emmission tomography (PET) is a diagnostic imaging technique based on the detection of radioactive emission. For that purpose a radioactive tracer (radiotracer) has to be injected into the patient's peripheral vein. PET is especially useful for monitoring the relative change of disease processes over time.Find more statistics on other topics about Austria with key insights such as number of pharmacists and number of hospital beds available.

no abstract provided

China Competitiveness Indicator: Relative Consumer Prices: Overall Weights data was reported at 88.011 2015=100 in 2025. This records a decrease from the previous number of 90.249 2015=100 for 2024. China Competitiveness Indicator: Relative Consumer Prices: Overall Weights data is updated yearly, averaging 79.300 2015=100 from Dec 1995 (Median) to 2025, with 31 observations. The data reached an all-time high of 100.000 2015=100 in 2015 and a record low of 58.445 2015=100 in 1995. China Competitiveness Indicator: Relative Consumer Prices: Overall Weights data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: Trade Statistics: Competitiveness Indicators In International Trade: Forecast: Non OECD Member: Annual. CPIDR - Indicator of competitiveness based on relative consumer prices Competitiveness-weighted relative consumer prices for the overall economy in dollar terms. .Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 53 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position.Index, OECD reference year OECD calculation, see OECD Economic Outlook database documentation