My Grandpa asked if the programs I was using could calculate his Golf League’s handicaps, so I decided to play around with SQL and Google Sheets to see if I could functionally recreate what they were doing.

The goal is to calculate a player’s handicap, which is the average of the last six months of their scores minus 29. The average is calculated based on how many games they have actually played in the last six months, and the number of scores averaged correlates to total games. For example, Clem played over 20 games so his handicap will be calculated with the maximum possible scores accounted for, that being 8. Schomo only played six games, so the lowest 4 will be used for their average. Handicap is always calculated with the lowest available scores.

This league uses Excel, so upon receiving the data I converted it into a CSV and uploaded it into bigQuery.

First thing I did was change column names to best represent what they were and simplify things in the code. It is much easier to remember ‘someone_scores’ than ‘int64_field_number’. It also seemed to confuse SQL less, as int64 can mean something independently. (ALTER TABLE grandpa-golf.grandpas_golf_35.should only need the one RENAME COLUMN int64_field_4 TO schomo_scores;)

To Find the average of Clem’s scores: SELECT AVG(clem_scores) FROM grandpa-golf.grandpas_golf_35.should only need the one LIMIT 8; RESULT: 43.1

Remembering that handicap is the average minus 29, the final computation looks like: SELECT AVG(clem_scores) - 29 FROM grandpa-golf.grandpas_golf_35.should only need the one LIMIT 8; RESULT: 14.1

Find the average of Schomo’s scores: SELECT AVG(schomo_scores) - 29 FROM grandpa-golf.grandpas_golf_35.should only need the one LIMIT 6; RESULT: 10.5

This data was already automated to calculate a handicap in the league’s excel spreadsheet, so I asked for more data to see if i could recreate those functions.

Grandpa provided the past three years of league data. The names were all replaced with generic “Golfer 001, Golfer 002, etc”. I had planned on converting this Excel sheet into a CSV and manipulating it in SQL like with the smaller sample, but this did not work.

Immediately, there were problems. I had initially tried to just convert the file into a CSV and drop it into SQL, but there were functions that did not transfer properly from what was functionally the PDF I had been emailed. So instead of working with SQL, I decided to pull this into google sheets and recreate the functions for this spreadsheet. We only need the most recent 6 months of scores to calculate our handicap, so once I made a working copy I deleted the data from before this time period. Once that was cleaned up, I started working on a function that would pull the working average from these values, which is still determined by how many total values there were. This correlates as follows: for 20 or more scores average the lowest 8, for 15 to 19 scores average the lowest 6, for 6 to 14 scores average the lowest 4 and for 6 or fewer scores average the lowest 2. We also need to ensure that an average value of 0 returns a value of 0 so our handicap calculator works. My formula ended up being:

=IF(COUNT(E2:AT2)>=20, AVERAGE(SMALL(E2:AT2, ROW(INDIRECT("1:"&8)))), IF(COUNT(E2:AT2)>=15, AVERAGE(SMALL(E2:AT2, ROW(INDIRECT("1:"&6)))), IF(COUNT(E2:AT2)>=6, AVERAGE(SMALL(E2:AT2, ROW(INDIRECT("1:"&4)))), IF(COUNT(E2:AT2)>=1, AVERAGE(SMALL(E2:AT2, ROW(INDIRECT("1:"&2)))), IF(COUNT(E2:AT2)=0, 0, "")))))

The handicap is just this value minus 29, so for the handicap column the script is relatively simple: =IF(D2=0,0,IF(D2>47,18,D2-29)) This ensures that we will not get a negative value for our handicap, and pulls the basic average from the right place. It also sets the handicap to zero if there are no scores present.

Now that we have our spreadsheet back in working order with our new scripts, we are functionally done. We have recreated what my Grandpa’s league uses to generate handicaps.

These data are mainly obtained from the sliceomatic software for the measurements of angles, lever arms and volume of reconstructions. The ratios have been calculated on excel

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Reinforcement learning (RL) has demonstrated significant potential in social robot autonomous navigation, yet existing research lacks in-depth discussion on the feasibility of navigation strategies. Therefore, this paper proposes an Integrated Decision-Control Framework for Social Robot Autonomous Navigation (IDC-SRAN), which accounts for the nonlinearity of social robot model and ensures the feasibility of decision-control strategy. Initially, inverse reinforcement learning (IRL) is employed to tackle the challenge of designing pedestrian walking reward. Subsequently, the Four-Mecanum-Wheel Robot dynamic model is constructed to develop IDC-SRAN, resolving the issue of dynamics mismatch of RL system. The actions of IDC-SRAN are defined as additional torque, with actual torque and lateral/longitudinal velocities integrated into the state space. The feasibility of the decision-control strategy is ensured by constraining the range of actions. Furthermore, a critical challenge arises from the state delay caused by model transient characteristics, which complicates the articulation of nonlinear relationships between states and actions through IRL-based rewards. To mitigate this, a driving-force-guided reward is proposed. This reward guides the robot to explore more appropriate decision-control strategies by expected direction of driving force, thereby reducing non-optimal behaviors during transient phases. Experimental results demonstrate that IDC-SRAN achieves peak accelerations approximately 8.3% of baseline methods, significantly enhancing the feasibility of decision-control strategies. Simultaneously, the framework enables goal-oriented autonomous navigation through active torque modulation, attaining a task completion rate exceeding 90%. These outcomes further validate the intelligence and robustness of the proposed IDC-SRAN.

Modeled ground magnetic data was extracted from the Pan American Center for Earth and Environmental Studies database at http://irpsrvgis08.utep.edu/viewers/Flex/GravityMagnetic/GravityMagnetic_CyberShare/ on 2/29/2012. The downloaded text file was then imported into an Excel spreadsheet. This spreadsheet data was converted into an ESRI point shapefile in UTM Zone 13 NAD27 projection, showing location and magnetic field strength in nano-Teslas. This point shapefile was then interpolated to an ESRI grid using an inverse-distance weighting method, using ESRI Spatial Analyst. The grid was used to create a contour map of magnetic field strength.

In this dataset, one excel file (with three sheets) and three csv files are provided. The data included in the excel file is the same as the one provided in three csv files. Data file named ‘Data Membranes – Excel file’ consists of three sheets containing raw data used for all analyzed values included in the manuscript. Three separate sheets are named ‘SW30HR’, ‘Desal-5 DK’ and ‘NF270’ and the names represent commercial names of the membranes used in the study. Data shown in each sheet represents data obtained for the membrane included in the sheet name. Each sheet contains input parameters data, including membrane used, operating pressure, contaminants of emerging concern used, contaminants of emerging concern concentration, operating pH and, only in sheet ‘SW30HR’, concentration of NaCl. Additionally, each sheet contains data related to concentration values in permeate and retentate, calculated rejection value in percentages (based on concentration values in permeate and retentate), as well as calculated positive and negative rejection error.

T1 values for intraobserver reproducibility assessment; Excel data with manual ROI placement by observer 1.

T1 values for interobserver reproducibility assessment; Excel data with manual ROI placement by observer 1 and 2.

This collection of 9-second raster data was compiled for use in modelling biodiversity pattern by developers engaged in supporting the New South Wales Biodiversity Indicators Program. Substrate and landform data derive from existing collections and have been altered from their native format to fill missing and erroneous data gaps as described in the lineage. Climate data were derived using existing methods as described in the lineage. Masks derived or adopted for use in processing the data are included in this collection. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.
Lineage: The abiotic environmental data in this collection are grouped by broad type - climate, substrate and landform. Datasets are provided in separate compressed folders (*.zip or *.7z). An excel spreadsheet is included with the collection that list and briefly describes all datasets and their source URLs, and the processing location of the data in the CSIRO project archive. A lineage document summarises the mask and gap filling processes. Mask data were developed from existing spatial boundary data including Australian coastline, State and administration boundaries, and previous raster modelling masks for the NARCLIM region. The data gap filling process was conducted in three stages (python processing scripts are included in this collection). In the first stage, the process used a 10 cell Inverse Distance Weighted (IDW) algorithm to fill no Data areas with data. The IDW algorithm used the distance of data values in the search radius as inverse weights in a neighbourhood average. To deal with remaining larger gaps, a second stage IDW was run on the outputs of the first stage with an increased radius of 500 cells. Any remaining data gaps were filled with a global data average. This process of data filling may make the data unsuitable for other uses and should be carefully considered before use. Images of each dataset are provided in the collection for ease of reference. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.

Environmental data from long-term monitoring sites in St. John, USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Methodology can be found in paper (Gross, K. and Edmunds, P. J. 2015). awards_0_award_nid=55191 awards_0_award_number=DEB-0841441 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0841441&HistoricalAwards=false awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=Saran Twombly awards_0_program_manager_nid=51702 awards_1_award_nid=562593 awards_1_award_number=DEB-1350146 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_1_funder_name=NSF Division of Environmental Biology awards_1_funding_acronym=NSF DEB awards_1_funding_source_nid=550432 awards_1_program_manager=Betsy Von Holle awards_1_program_manager_nid=701685 cdm_data_type=Other comment=Environmental data P. Edmunds, PI Version 14 September 2018 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.735088.1 infoUrl=https://www.bco-dmo.org/dataset/735088 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/735088 param_mapping={'735088': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/735088/parameters people_0_affiliation=California State University Northridge people_0_affiliation_acronym=CSU-Northridge people_0_person_name=Peter J. Edmunds people_0_person_nid=51536 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=North Carolina State University people_1_affiliation_acronym=NCSU people_1_person_name=Kevin Gross people_1_person_nid=535324 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=St. John LTREB,RUI-LTREB projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI 10.1007/s00442-013-2871-9. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Lenz EA, Brown D, Didden C, Arnold A, Edmunds PJ. The distribution of hermit crabs and their gastropod shells on shallow reefs in St. John, US Virgin Islands. Bulletin of Marine Science 90(2):681-692. https://dx.doi.org/10.5343/bms.2013.1049 Data at MCR-VINP.Download complete data for this publication (Excel file) 2013 Edmunds PJ. Decadal-scale changes in the community structure of coral reefs in St. John, US Virgin Islands. Marine Ecology Progress Series 489: 107-123. Data at MCR-VINP.Download complete data for this publication (zipped Excel files) 2013 Brown D, Edmunds PJ. Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. J. Exp Mar Biol Ecol 441: 62-70. doi: 10.1016/j.jembe.2013.01.013Millepora colony sizeMillepora cover - temps - storms 1992-2008Millepora cover 1992-2008seawater temperature USVI 1992-2008storms USVI 1992-2008Download complete data for this publication (Excel file) 2012 Brown D, Edmunds PJ. The hermit crab Calcinus tibicen lives commensally on Millepora spp. in St. John, United States Virgin Islands. Coral Reefs 32: 127-135. doi: 10.1007/s00338-012-0948-2crab abundance and coral sizecrab displacement behaviorcrab nocturnal surveyscrab predator avoidanceDownload complete data for this publication (Excel file) 2011 Green DH, Edmunds PJ. Spatio-temporal variability of coral recruitment on shallow reefs in St. John, US Virgin Islands. Journal of Experimenal Marine Biology and Ecology 397: 220-229. Data at MCR-VINP.Download complete data for this publication (Excel file) 2011 Colvard NB, Edmunds PJ. (2011) Decadal-scale changes in invertebrate abundances on a Caribbean coral reef. Journal of Experimental Marine Biology and Ecology. 397(2): 153-160. doi: 10.1016/j.jembe.2010.11.015benthic invert codesinverts - Tektite and Yawzi Ptinverts - pooledDownload complete data for this publication (Excel file) projects_0_end_date=2014-04 projects_0_geolocation=St. John, U.S. Virgin Islands; California State University Northridge projects_0_name=LTREB Long-term coral reef community dynamics in St. John, USVI: 1987-2019 projects_0_project_nid=2272 projects_0_project_website=http://coralreefs.csun.edu/ projects_0_start_date=2009-05 projects_1_acronym=RUI-LTREB projects_1_description=Describing how ecosystems like coral reefs are changing is at the forefront of efforts to evaluate the biological consequences of global climate change and ocean acidification. Coral reefs have become the poster child of these efforts. Amid concern that they could become ecologically extinct within a century, describing what has been lost, what is left, and what is at risk, is of paramount importance. This project exploits an unrivalled legacy of information beginning in 1987 to evaluate the form in which reefs will persist, and the extent to which they will be able to resist further onslaughts of environmental challenges. This long-term project continues a 27-year study of Caribbean coral reefs. The diverse data collected will allow the investigators to determine the roles of local and global disturbances in reef degradation. The data will also reveal the structure and function of reefs in a future with more human disturbances, when corals may no longer dominate tropical reefs. The broad societal impacts of this

A Microsoft Excel file containing the data that forms the figures in the main text of the publication. The Microsoft Excel file contains the ultrafast transient absorption and photoluminescence data for TXO-TPA and 4CzIPN, presented in wavelength (nm) and time (ps). Also included is the steady state Raman and impulsive vibrational spectra of TXO-TPA and 4CzIPN at 0.5, 3, and 10 ps (in wavenumbers). The full datasets for the quantum-chemical molecular dynamics simulations of TXO-TPA are also presented. See the main manuscript for more details.

File List Supplement1.xls (md5: 4202b5bccb5ee828f646f50530394c47)

  Please be advised that the ESA cannot guarantee the forward migration of proprietary file formats such as Excel (.xls) documents.
Description
  SupplementA.xls is an Excel spreadsheet containing 5 sheets with example calculations. The first 4 sheets (labeled Model 1 - Model 4) contain calculations for models considered in APPLICATION TO YELLOWSTONE BISON:
  Model 1: Makes no assumptions about equality of survival rates for different age classes.
  Model 2: Assumes survival rates are equal for ages 0–1, 2–3, 4–5, 6–7, 8–9, 10–11, 12–13.
  Model 3: Assumes survival rates are equal for ages 0–1, 2–3, 4–5, 6–11, 12–13.
  Model 4: Assumes survival rates are equal for ages 0–13.
  The last sheet (labeled 3 Years) contains calculations for a hypothetical example with 3 age classes and 3 years of data, and no assumptions about equality of survival rates.

Data describing Gorgonia ventalina interactions on St. John, Virgin Islands in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Methodology\u00a0from\u00a0Gambrel, B.\u00a0and\u00a0Lasker, H.R., 2016

While colony asymmetry was observed in many species, the planar form of G.\u00a0ventalina\u00a0was particularly conducive to quantifying colony size and asymmetry. The surface areas of both symmetric and asymmetric G.\u00a0ventalina\u00a0colonies were measured to evaluate the extent of asymmetry. We did not distinguish between\u00a0intra- and\u00a0inter- specific\u00a0competitive interactions with asymmetric G.\u00a0ventalina\u00a0since our goal was to compare the sizes of asymmetric and symmetric colonies.

We sampled at Europa Bay, where similar numbers of symmetric and asymmetric G.\u00a0ventalina\u00a0colonies were found. We searched for G.\u00a0ventalina\u00a0within the same 500 m2 area as the belt transects, and we selected colonies based on their health (colonies overgrown with algae or\u00a0hydrocorals\u00a0were excluded from the study), size class (15\u221224 cm, 25\u221234 cm and 35\u221244 cm tall) and the presence or absence of asymmetry, since we wanted similar numbers of colonies in each category. A total of 142 G.\u00a0ventalina\u00a0colonies (78 symmetric and 64 asymmetric) were photographed. Each image included a 1 m2 quadrat and a ruler. awards_0_award_nid=562090 awards_0_award_number=OCE-1334052 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1334052 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Gorgonia Asymmetry Data from Europa Bay Howard Lasker, PI Version 14 October 2016 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.662645.1 Easternmost_Easting=-64.72988 geospatial_lat_max=18.3166 geospatial_lat_min=18.3166 geospatial_lat_units=degrees_north geospatial_lon_max=-64.72988 geospatial_lon_min=-64.72988 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/662645 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/662645 Northernmost_Northing=18.3166 param_mapping={'662645': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/662645/parameters people_0_affiliation=State University of New York at Buffalo people_0_affiliation_acronym=SUNY Buffalo people_0_person_name=Howard Lasker people_0_person_nid=562092 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Hannah Ake people_1_person_nid=650173 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=St. John LTREB,VI Octocorals projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI 10.1007/s00442-013-2871-9. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Lenz EA, Brown D, Didden C, Arnold A, Edmunds PJ. The distribution of hermit crabs and their gastropod shells on shallow reefs in St. John, US Virgin Islands. Bulletin of Marine Science 90(2):681-692. https://dx.doi.org/10.5343/bms.2013.1049 Data at MCR-VINP.Download complete data for this publication (Excel file) 2013 Edmunds PJ. Decadal-scale changes in the community structure of coral reefs in St. John, US Virgin Islands. Marine Ecology Progress Series 489: 107-123. Data at MCR-VINP.Download complete data for this publication (zipped Excel files) 2013 Brown D, Edmunds PJ. Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. J. Exp Mar Biol Ecol 441: 62-70. doi: 10.1016/j.jembe.2013.01.013Millepora colony sizeMillepora cover - temps - storms 1992-2008Millepora cover 1992-2008seawater temperature USVI 1992-2008storms USVI 1992-2008Download complete data for this publication (Excel file) 2012 Brown D, Edmunds PJ. The hermit crab Calcinus tibicen lives commensally on Millepora spp. in St. John, United States Virgin Islands. Coral Reefs 32: 127-135. doi: 10.1007/s00338-012-0948-2crab abundance and coral sizecrab displacement behaviorcrab nocturnal surveyscrab predator avoidanceDownload complete data for this publication (Excel file) 2011 Green DH, Edmunds PJ. Spatio-temporal variability of coral recruitment on shallow reefs in St. John, US Virgin Islands. Journal of Experimenal Marine Biology and Ecology 397: 220-229. Data at MCR-VINP.Download complete data for this publication (Excel file) 2011 Colvard NB, Edmunds PJ. (2011) Decadal-scale changes in invertebrate abundances on a Caribbean coral reef. Journal of Experimental Marine Biology and Ecology. 397(2): 153-160. doi: 10.1016/j.jembe.2010.11.015benthic invert codesinverts - Tektite and Yawzi Ptinverts - pooledDownload complete data for this publication (Excel file) projects_0_end_date=2014-04 projects_0_geolocation=St. John, U.S. Virgin Islands; California State University Northridge projects_0_name=LTREB Long-term coral reef community dynamics in St. John, USVI: 1987-2019 projects_0_project_nid=2272 projects_0_project_website=http://coralreefs.csun.edu/ projects_0_start_date=2009-05 projects_1_acronym=VI Octocorals projects_1_description=The recent past has not been good

Dryad_MatedPair_DataThis spreadsheet (MS Excel format) contains data related to raven mate pairing behavior with respect to their mtDNA haplotypes. See the associated ReadMe file for addition details.

Quantifying lithospheric strength is essential to better understand seismicity in continental regions. In the manuscript “Strength and Stress Evolution of an Actively Exhuming Low-Angle Normal Fault, Woodlark Rift, SE Papua New Guinea”, we estimate differential stresses and principal stress orientations that drive rapid slip (~10 mm/yr) on the active Mai’iu fault (dipping ~16-24° at the Earth’s surface) in Papua New Guinea. We compile stress-depth snapshots by taking advantage of space-for-time relationships provided by progressive slip localization within the cooling and exhuming footwall of the Mai’iu fault. Estimated differential stresses are based on the mechanical twinning and/or recrystallized grain-size of deformed calcite veins that cross-cut the sequentially formed fault rock units (mylonites, foliated cataclasites, ultracataclasites and gouges). The orientation of principal stresses acting on the fault zone are estimated using stress-inversion techniques on crystallographic data for calcite-twins collected by electron backscatter diffraction (EBSD), and on fault-slip data of late outcrop-scale brittle faults cross-cutting the footwall and hangingwall of the Mai’iu fault. The data repository provides the raw dataset used for the paleostress analyses in this study from which we derive the fault’s peak strength (140–185 MPa) and the integrated strength of the extending brittle crust. The raw dataset includes:

(1) Calcite E-Twin Analysis: 12 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), overview maps (.jpeg) of all analyzed calcite grains, excel-sheets with orientation and twin morphology data on the analyzed calcite grains, and EBSD Euler conversion output files (_out.xlsx; see below); (2) Calcite Grain-Size Piezometer: 8 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), multiple overview maps (.png) of the analyzed calcite veins, and grain-size histograms of the relict and recrystallized grains; (3) Calcite Paleostress Analysis: twinning data that was made analogous to fault-slip data (.fdt) and best-fit stress orientations as calculated by the multiple inverse method (.mi4); (4) EBSD Euler Conversion: MATLAB code to calculate slip plane (e-plane) and glide direction from calcite host-twin pairs and to transform EBSD acquired orientation data from a sample reference frame into a geographic system; (5) Mai'iu Fault Structural Data: an excel-sheet with all structural data collected in the Suckling-Dayman Metamorphic Core Complex during the field campaigns in 2014, 2015 and 2016 (includes sample locations, fault-slip data, bedding data of the Gwoira Conglomerates, etc…; version 7, date: 26.09.2016).

All geothermometric data and explanations on how to reproduce the estimated paleostresses (using the provided raw dataset) can be found in the main manuscript. This unique dataset provides insights into the strength and stress evolution of the Woodlark Rift, Papua New Guinea.

This study investigates pricing and coordination strategies for a dual-channel supply chain (DCSC), considering technological innovations in emergencies. We have established the DCSC model consisting of a manufacturer, a retailer, and an E-commerce platform (ECP). Whether manufacturers choose to invest in technological innovation during emergencies can be divided into traditional production mode and technological innovation mode. Using the reverse induction method to solve the Stackelberg game problem, explore the pricing and channel selection strategies of each member in a DCSC under different modes. In addition, a revenue-sharing contract for a DCSC under emergencies was designed and improved. Research has shown that under emergencies, consumers’ technological innovation preference can increase the profits of each member in the DCSC and manufacturers’ technological innovation level. Manufacturers are more willing to choose technological innovation mode rather than traditional production mode. However, an increase in the commission rate of ECP can hinder the level of technological innovation of manufacturers and affect the issue of choosing between offline channel and ECP channel. Specifically, when the commission rate exceeds a certain threshold, the offline channel should be chosen. Finally, traditional revenue-sharing contracts fail to effectively coordinate DCSC that incorporate technological innovation during emergencies. To address this limitation, an improved revenue-sharing contract is proposed, which enhances the level of technological innovation while achieving Pareto improvements within the DCSC.

Contains the Reference Model 5 (RM5) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM5 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014.

The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting.

Reference Model 5 (RM5) is a type of floating, oscillating surge wave energy converter (OSWEC) that utilizes the surge motion of waves to generate electrical power. The reference wave energy resource for RM5 was measurement data from a National Data Buoy Center (NDBC) buoy near Eureka, in Humboldt County, California. The flap was designed to rotate against the supporting frame to convert wave energy into electrical power from the relative rotational motion induced by incoming waves. The RM5 design is rated at 360 kilowatts (kW), uses a flap of 25 m in width and 19 m in height (16 m in draft), and the distance from the top of the water surface piercing flap to the mean water surface (freeboard) is 1.5 m. The flap is connected to a shaft with a 3-m diameter that rotates against the supporting frame. The supporting frame is assumed to have an outer diameter of 2 m, and the total length of the device structure is 45 m. The RM5 OSWEC was designed for deep-water deployment, at depths between 50 m and 100 m, and was tension-moored to the seabed.

Quantifying lithospheric strength is essential to better understand seismicity in continental regions. In the manuscript “Using Syntectonic Calcite Veins to Reconstruct the Strength Evolution of an Active Low‐Angle Normal Fault, Woodlark Rift, SE Papua New Guinea”, we estimate differential stresses and principal stress orientations that drive rapid slip on the active Mai’iu fault (dipping ~16-24° at the Earth’s surface) in Papua New Guinea. We compile stress-depth snapshots by taking advantage of space-for-time relationships provided by progressive slip localization within the cooling and exhuming footwall of the Mai’iu fault. Estimated differential stresses are based on the mechanical twinning and/or recrystallized grain-size of deformed calcite veins that cross-cut the sequentially formed fault rock units (mylonites, foliated cataclasites, ultracataclasites and gouges). The orientation of principal stresses acting on the fault zone are estimated using stress-inversion techniques on crystallographic data for calcite-twins collected by electron backscatter diffraction (EBSD), and on fault-slip data of late outcrop-scale brittle faults cross-cutting the footwall and hangingwall of the Mai’iu fault. The data repository provides the raw dataset used for the paleostress analyses in this study from which we derive the fault’s peak strength (140–185 MPa) and the integrated strength of the extending brittle crust. The raw dataset includes:

(1) Calcite E-Twin Analysis: 12 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), overview maps (.jpeg) of all analyzed calcite grains, excel-sheets with orientation and twin morphology data on the analyzed calcite grains, and EBSD Euler conversion output files (_out.xlsx; see below); (2) Calcite Grain-Size Piezometer: 8 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), multiple overview maps (.png) of the analyzed calcite veins, and grain-size histograms of the relict and recrystallized grains; (3) Calcite Paleostress Analysis: twinning data that was made analogous to fault-slip data (.fdt) and best-fit stress orientations as calculated by the multiple inverse method (.mi4); (4) EBSD Euler Conversion: MATLAB code to calculate slip plane (e-plane) and glide direction from calcite host-twin pairs and to transform EBSD acquired orientation data from a sample reference frame into a geographic system; (5) Mai'iu Fault Structural Data: an excel-sheet with all structural data collected in the Suckling-Dayman Metamorphic Core Complex during the field campaigns in 2014, 2015 and 2016 (includes sample locations, fault-slip data, bedding data of the Gwoira Conglomerates, etc…; version 7, date: 26.09.2016).

All geothermometric data and explanations on how to reproduce the estimated paleostresses (using the provided raw dataset) can be found in the main manuscript. This unique dataset provides insights into the strength and stress evolution of the Woodlark Rift, Papua New Guinea.

The excel file contains data about 10-18 years old students' affect/ well-being during lock-down in Pakistan. The cells in the top row contain the items. The numbers in the columns 'C' to 'J' represent following. 1 = Very Often; 2 = Often; 3 = Sometimes; 2 = Rarely; 1 = Never The numbers in the column 'K' have been coded in reverse order. Column 'L' contains response of the question "Have you started fighting more with your siblings?" Column 'M' and 'N' include open-ended, short responses.