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.

Nigeria adopted dolutegravir (DTG) as part of first line (1L) antiretroviral therapy (ART) in 2017. However, there is limited documented experience using DTG in sub-Saharan Africa. Our study assessed DTG acceptability from the patient’s perspective as well as treatment outcomes at 3 high-volume facilities in Nigeria. This is a mixed method prospective cohort study with 12 months of follow-up between July 2017 and January 2019. Patients who had intolerance or contraindications to non-nucleoside reverse-transcriptase inhibitors were included. Patient acceptability was assessed through one-on-one interviews at 2, 6, and 12 months following DTG initiation. ART-experienced participants were asked about side effects and regimen preference compared to their previous regimen. Viral load (VL) and CD4+ cell count tests were assessed according to the national schedule. Data were analysed in MS Excel and SAS 9.4. A total of 271 participants were enrolled on the study, the median age of participants was 45 years, 62% were female. 229 (206 ART-experienced, 23 ART-naive) of enrolled participants were interviewed at 12 months. 99.5% of ART-experienced study participants preferred DTG to their previous regimen. 32% of particpants reported at least one side effect. “Increase in appetite” was most frequently reported (15%), followed by insomnia (10%) and bad dreams (10%). Average adherence as measured by drug pick-up was 99% and 3% reported a missed dose in the 3 days preceding their interview. Among participants with VL results (n = 199), 99% were virally suppressed (

Excel file containing source data for Figs 1–4, 6, B and C in S1 Text.

Literature search methods: A systematic review was done in November 2014 using the databases Web of Science and Google Scholar to collect and analyse the primary literature to date written about tool use and tool making behaviour in non-human animals. The search for publications on Google Scholar was done using the search terms “tool+using+making+animals”, including only articles, written in a non-limited period of time, sorted by relevance. Since Google Scholar provided a large amount of articles in a descending order from more relevant to less relevant, we detected relevant articles doing a first manual scan looking at the title and at the abstract until relevance was consistent, producing a total of 23 possible publications.The search for literature that was executed using the database Web of Science was done using the search terms “Tool*” (Topic) AND “Use* OR Utilization*” (Topic) AND “Mak*” (Topic) AND “Animal*”(Topic). This produced a result of 316 possible publications. Then we refined the results using the following search categories: “Behavioral Sciences”, “Ecology”, “Zoology”. We also selected only articles. After that, 9 articles were left. Then these underwent a title and abstract scan for relevance to the specific topic. The full text of the remaining articles was processed, and the articles that did not provide specific information about the occurrence of tool use and/or tool behaviour in animals were excluded. We also excluded all the secondary literature as reviewed primary literature without providing its own data. Articles whose content was not focused on the specified topic and articles whose data provided were not enough were also excluded. Of the 339 initial publications, 32 were screened: 2 were removed for not being primary research articles, 24 were directly related to the topic, 6 were excluded with reasons listed above. The remaining 24 studies included in the analysis were composed of experiments from 1973 to 2014. Out of 24 articles, 4 were written in 2005, 2 in 1982, 2 in 1990, 2 in 1994, 2 in 2003 and 2 in 2014. All articles that were included in this review were published in English in a total of 17 journals. Journal of Comparative Psychology published 4 articles out of 24 and Primates published 3. Analysis of the literature: Studies were coded by the geographical location (country's name), the duration (total lenght of the research measured in months), the type of the experiment performed ( observational, experimental), the common name of the animal observed or used as experimental subject, the activity that was the scope of the tool use behaviour, the kind of tool being used.

pone.0284767.t001 - High acceptability and viral suppression rate for first-Line patients on a dolutegravir-based regimen: An early adopter study in Nigeria

Excel sheet containing numerical data used to generate Figs 2–8 and S1.

Excel spreadsheet containing, in separate sheets, the underlying numerical data presented in the manuscript.

Original data set for Fukushima et al study on mitochondrial ROS generation under conditions simulating early reperfusion injury. Microsoft Excel spreadsheet, with one tab/sheet for each figure. See "Read Me" tab for more details. Note - Tukey's post-hoc tests used "Real Statistics" Excel plug-in. Underlying calculations are not included, since they will only be visible to those with the plug-in installed in Excel.

This dataset provides comprehensive information on road intersection crashes involving motorcycles (Motor tricycle, Motorcycle: under 125cc, Motorcycle: Above 125cc, Quadru-cycle) that have resulted in injuries recognised as "high-high" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in 33% of the total "high-high" cluster motorcycle road intersection crashes resulting in injuries for the years 2017, 2018 and 2019. The dataset is meticulously organised according to confidence metric values presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 29,8 KBNumber of Files: The dataset contains a total of 576 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-high" cluster fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes involving a motorcycle resulting in injuries that occurred within the "high-high" cluster fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,30 support metric value. Consequently, commonly occurring crash attributes among at least 33% of the "high-high" cluster road intersection motorcycle crashes resulting in injuries were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

Excel sheet containing annotations of the publicly available P. multocida genomes evaluated in Fig 1.

Summary statistics of plasma HIV-1 RNA viral load results.

Summary statistics of the CD4+ cell count disaggregated by ART status.