The upload contains an Eclipse Excel spreadsheet that is enhanced from a Reverse Beacon Network (RBN) export, and sorted by receiving callsign and Zulu PM time during the eclipse and 15 minutes before and for a period from 8:45 to 12:30, local Corvallis time. The experiment looked at the RBN sites who received CW signals from the callsign WG0R, which was transmitting test messages at 100 Watts using the below station resources.

The transmission resources are: Elecraft KX3, KXPA100, and PX3 transceiver, 100 Watt amplifier, and Panadapter, driving a Carolina Windom Antenna at 10 meterrs above ground level, strung at 150 to 330 degrees in two Oak trees.

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

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.

This zip file contains the RT-qPCR results from the final report ST-2023-20026-01: Investigation of environmental RNA (eRNA) as a detection method for dreissenid mussels and other invasive species.

RT-qPCR (reverse transcriptase quantification polymerase chain reaction) analysis was conducted on eRNA (environmental ribosomal nucleic acid) isolated from water samples collected at Canyon Reservoir, AZ. The goal of the project was to test out three different RNA preservation methods and three different RNA extraction methods. RT-qPCR was used to detect the presence of eRNA in the samples. The analysis was conducted using the CFX Maestro instrument. Included in the zip file is the CFX Maestro software information. The Cq (quantification value) was obtained using RT-qPCR for each sample, analyzed, and used to create the figures in the final report.

Following each RT-qPCR assay, all the files associated with the experiment were downloaded and saved. There are 14 folders, and each contain a series of Excel spreadsheets that contain the information on the RT-qPCR experiment. These Excel spreadsheets include the following data: ANOVA results, end point results, gene expression results, melt curve results, quantification amplification results, Cq results, plate view results, standard curve, and run information for each RT-qPCR analysis. Some of the folders also contain images of the amplification curve, melt curve, melt peak, and standard curve for the experiment.

The Cq values used in the report were taken from the quantification amplification file for each of the experiments. These Cq values were placed into the eRNA Data and Figures Excel spreadsheet. In this spreadsheet the Cq values were analyzed, and graphs were made with the data.

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 (

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.

The data set is derived from survey responses provided by individual participants in the study, which are stored in an Excel file. As the data is original, no modifications, coding, and/or reversing have been applied. Only numerical data from the survey questions (on a Likert scale) and written responses from the open-ended questions are stored in the file. For detailed information regarding the survey questions, it is recommended to contact the project leader via email. All names have been removed from the data set to safeguard participants' anonymity.

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.

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

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.

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

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

Background and objectivesAim of the current study was to assess the perception, preference, and practice of endodontists and restorative dentists at different locations around the world about dental magnification instruments.Materials and methodsA multicenter, cross-sectional study was ethically approved from the local committee of bioethics. After thorough literature search, a questionnaire was designed and validated. Later, the questionnaire was distributed to 10% (53 participants) of the total planned participants to conduct a pilot study. Based on the feedback from these participants, any ambiguities or discrepancies observed in the items and content of the questionnaire was modified. The questionnaire was assessed for its internal consistency as part of validating the items with Cronbach’s alpha of 0.80. The completed questionnaire with an informed consent form for the participant was administered to the endodontists and restorative dentists in three different geographical regions namely MENA (Middle East and Northern Africa), British-Isles, and Indian Sub-continent using WhatsApp through the snowball convenience sampling technique.ResultsMajority of the participants were male (56.5%) and in the age group of 25–35 years (30.3%). About 68.9% were from Indian sub-continent, followed by the British-Isles (16.5%) and the least (14.6%) were from the MENA region. By large, the participants of the present study, strongly agreed that dental magnification devices improved ergonomics, quality of work, and should be considered as standard of care in modern endodontic. Flip-up magnifiers (51.1%) and medium (8x-16x) magnification were preferred by majority of the participants. About 46.3% of specialist reported that they always used devices for all operative and endodontic procedures, especially while locating hidden and canals and negotiating calcified canals. Participants practicing in British-Isles have 2.42 times (P<0.05) higher adequate perception with reference participants in Indian sub-continent. Additionally, participants with fellowship have 2.77 times more (P<0.01) adequate perception with reference to their counterparts with a master’s degree.ConclusionsMost of the participants believe that dental magnification devices enhance the prognosis and quality of treatment of possibly all operative and endodontics procedures. Thus, emphasized on the inclusion of devices in the postgraduate curriculum and signifies the role of continuing dental education for specialist and dental assistant handling devices. However, multicenter studies with larger sample is required for generalizing the results.

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