Mean reaction time (RTs; in ms) followed by the Standard Error of the Mean (SEM) and mean proportion of errors (ERR; in %) followed by standard deviation (SD), depicted separately for same and switched additions (+) and subtractions (−), for the original symbol-switching task (from Experiment 1), the symbol-switching task with letters (from Experiment 2), and the stimulus offset condition from Experiment 2, where the letters offset upon response (Fast Offset).

An Open Context "subjects" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Region" record is part of the "Open Context" data publication.

This data set includes 956 polygons labeled with a sensitivity-unit code that represents the sensitivity of ground water to contamination in Lawrence County, SD. This data set is a result of a larger work (WRIR 00-4103 cited above), which includes a paper plate titled: "Map showing sensitivity of ground-water to contamination in Lawrence County, South Dakota." This data set is part of the digital data that was used to create that map. The sensitivity-unit code is an attribute that consists of a letter followed by three numerical digits, which characterizes sensitivity to contamination. Letter codes that begin with upper case letters (A-Z) and continue with lower case letters (a-s) represent characteristics of the rock and sediments, with 'A' being most sensitive and 's' being least sensitive. The first digit represents recharge rate with 1 being the most sensitive and 4 the least sensitive. Three quantitative categories (1-3) and two qualitative categories (4,5) represent depth to water. Groups 1 through 3 represent areas where data was available to estimate depth-to-water with 1 most sensitive and 3 least sensitive. Qualitative categories 4 and 5 represent areas that only can be compared to each other with 4 being the most sensitive. The third digit represents land-surface slope with 1 being the most sensitive and 5 being the least sensitive. An additional attribute, hydrologic setting, represents areas with common hydrologic characteristics. These 11 hydrologic settings are represented by a letter code symbol. The process step section below describes the attributes in more detail and how the attributes were developed from source data. The source data includes digital maps that characterize the geology, precipitation distribution, and water levels, which have been compiled at 1:100,000 scale and published in 1999 and 2000 as part of the Black Hills Hydrology Study. USGS Digital elevation models were used to describe land-surface altitudes. This data set has been archived at the USGS Water Resources National Spatial Data Information Node.

Source: German Socio-Economic Panel, pooled data for 2006 and 2012. Abbreviations: SDT—Symbol-Digit Test (cognition); PCS—composite score physical health; MCS—composite score mental health. The PCS, MCS, and SDT scores have been z-standardized with a mean of 50 and a SD of 10.Sample Characteristics.

Sample: First-time participants in cognitive testing in the SOEP in 2006 or 2012. Population aged 50–90 at the time of interview. Abbreviations: SDT—Symbol-Digit Test (cognition); PCS—composite score physical health; MCS—composite score mental health. The PCS, MCS, and SDT scores have been z-standardized with a mean of 50 and a SD of 10.Significance levels*** p

Version 10.0 (Alaska, Hawaii and Puerto Rico added) of these data are part of a larger U.S. Geological Survey (USGS) project to develop an updated geospatial database of mines, mineral deposits, and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, have been digitized from the 7.5-minute (1:24,000, 1:25,000-scale; and 1:10,000, 1:20,000 and 1:30,000-scale in Puerto Rico only) and the 15-minute (1:48,000 and 1:62,500-scale; 1:63,360-scale in Alaska only) archive of the USGS Historical Topographic Map Collection (HTMC), or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. The compilation of 725,690 point and polygon mine symbols from approximately 106,350 maps across 50 states, the Commonwealth of Puerto Rico (PR) and the District of Columbia (DC) has been completed: Alabama (AL), Alaska (AK), Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Connecticut (CT), Delaware (DE), Florida (FL), Georgia (GA), Hawaii (HI), Idaho (ID), Illinois (IL), Indiana (IN), Iowa (IA), Kansas (KS), Kentucky (KY), Louisiana (LA), Maine (ME), Maryland (MD), Massachusetts (MA), Michigan (MI), Minnesota (MN), Mississippi (MS), Missouri (MO), Montana (MT), Nebraska (NE), Nevada (NV), New Hampshire (NH), New Jersey (NJ), New Mexico (NM), New York (NY), North Carolina (NC), North Dakota (ND), Ohio (OH), Oklahoma (OK), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC), South Dakota (SD), Tennessee (TN), Texas (TX), Utah (UT), Vermont (VT), Virginia (VA), Washington (WA), West Virginia (WV), Wisconsin (WI), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the U.S., but an approximate timeline of when these activities occurred. These data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. These data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.Datasets were developed by the U.S. Geological Survey Geology, Geophysics, and Geochemistry Science Center (GGGSC). Compilation work was completed by USGS National Association of Geoscience Teachers (NAGT) interns: Emma L. Boardman-Larson, Grayce M. Gibbs, William R. Gnesda, Montana E. Hauke, Jacob D. Melendez, Amanda L. Ringer, and Alex J. Schwarz; USGS student contractors: Margaret B. Hammond, Germán Schmeda, Patrick C. Scott, Tyler Reyes, Morgan Mullins, Thomas Carroll, Margaret Brantley, and Logan Barrett; and by USGS personnel Virgil S. Alfred, Damon Bickerstaff, E.G. Boyce, Madelyn E. Eysel, Stuart A. Giles, Autumn L. Helfrich, Alan A. Hurlbert, Cheryl L. Novakovich, Sophia J. Pinter, and Andrew F. Smith.USMIN project website: https://www.usgs.gov/USMIN

The Chester Archaeological Character Zones were created as part of the Chester Urban Archaeological Database (UAD) Project and encompass the Chester non parish area and civil parishes of Chester Castle and Great Boughton. They form part of the Chester Archaeological Plan which is part of the evidence base for the Cheshire West and Chester Local Plan. Each Archaeological Character Zone includes a character area statement which surmises the archaeological potential, type, significance and potential depth of deposits within that area, which is accessed via a hyperlink in the attribute data. Further information on the Chester Archaeological Plan and the UAD project see: http://www.cheshirearchaeology.org.uk/?page_id=156.

The first six character histories with the highest posterior probability (PPc) for each combination of E(T) = 1 and 5 and their respective SD(T).

Conservation Areas are areas designated for their special architectural or historic interest whose character or appearance is considered to be worth protecting or enhancing. The area’s character can be defined by its buildings and building materials; the network of roads, paths and boundaries and features such as parks and gardens. Conservation Areas provide broad protection to all the features within the area which contribute to its overall character.

Local Authorities, under the Planning (Listed Buildings and Conservation Areas) Act 1990, may designate an area as a Conservation Area. Notifications of new and amended Conservation Areas are issued by the relevant Local Authority as and when necessary and this dataset is updated by the Cheshire Archaeology Planning Advisory Service as soon as possible after the notification is received.

Different lowercase letters indicate significant differences among four study sites.

An Open Context "subjects" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Region" record is part of the "Open Context" data publication.

Association of FGF-23 with baseline cognitive vitality test scores.

Demographics and clinical characteristics by quartiles of FGF-23.

For age, the numbers are mean years for the dyslexia and control groups. For the Raven, the numbers are mean percentiles for the dyslexia and control groups. For reading fluency, the numbers represent means of items that the dyslexia and control groups answered correctly. For Chinese character recognition, the numbers are the numbers of characters children could use correctly in word composition.Characteristics of the participants, with standard deviation in parenthesis.

The mean and standard deviation, shown in brackets, of the behavioral measures and group comparison results.