Analysis of ‘NFL Combine - Performance Data (2009 - 2019)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/redlineracer/nfl-combine-performance-data-2009-2019 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains information from the NFL Combine (2009 to 2019), including the results from sports performance tests and draft outcomes.

Content

As sports statistics are in the public domain, this database was freely downloaded from https://www.pro-football-reference.com/

Acknowledgements

I appreciate the efforts of https://www.pro-football-reference.com/ in collating and hosting sports related data, and Kaggle for providing a platform for sharing datasets and knowledge.

Inspiration

This dataset is useful for beginners and intermediate users, where they can practice visualisations, analytics, imputation, data cleaning/wrangling, and classification modelling. For example: What are the variables of importance in predicing round pick or draft status? Which school has the highest number of players being drafted into NFL? What position type or player type is most represented at the NFL Combine? Do drafted and undrafted players perform differently on performance tests?

--- Original source retains full ownership of the source dataset ---

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates”. The use of surrogate training data can yield diminished performance of concentration estimation approaches. In order to evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from chemical surrogates.

This dataset is associated with the following publication: Pu, S., J. McCord, J. Bangma, and J. Sobus. Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 416: 1249-1267, (2024).

ABSTRACT Introduction Non-intelligent factors include learning habits, motivation, interest, emotion, attitude, and student characteristics. Many sports practices have demonstrated that creating excellent athletic performance and winning intense competition depends on various factors. Among them, physical quality is the physiological and material basis to ensure the quality of exercise. Movement technique is the essential condition. However, non-Intelligent factors are the internal motivators for both to function. Objective Analyze the non-Intelligent factors that affect the performance of volleyball players. Methods Several volleyball players were selected as research objects. The non-Intelligent factors that affect volleyball performance are analyzed by questionnaire survey and experimental method. Finally, this paper uses mathematical statistics to analyze the experimental data. Results Volleyball players are easily disturbed by external factors. These non-Intelligent factors can easily lead to large fluctuations in the athlete’s psychology. These reasons will affect the stability of volleyball players’ serving skills. Conclusion The non-Intelligent factors that affect the performance of volleyball players are the proficiency of serving technique, the degree of psychological relaxation, and the ability of emotional control. Level of evidence II; Therapeutic studies - investigation of treatment results.

As of the first half of 2024, the non-life insurances return on assets performance amounted to minus *** percent. In comparison, reinsurance return on assets performance reached **** percent over the same period.

These non-consolidated performance related pay figures are being published as part of the government's commitment to transparency. These figures are not official statistics but internal information published in the interests of transparency. They have not been reconciled centrally with any National Statistics.

Analysis of ‘Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/impapan/student-performance-data-set on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades.

Attribute Information:

# Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
2 sex - student's sex (binary: 'F' - female or 'M' - male)
3 age - student's age (numeric: from 15 to 22)
4 address - student's home address type (binary: 'U' - urban or 'R' - rural)
5 famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
6 Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
9 Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
11 reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
12 guardian - student's guardian (nominal: 'mother', 'father' or 'other')
13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16 schoolsup - extra educational support (binary: yes or no)
17 famsup - family educational support (binary: yes or no)
18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19 activities - extra-curricular activities (binary: yes or no)
20 nursery - attended nursery school (binary: yes or no)
21 higher - wants to take higher education (binary: yes or no)
22 internet - Internet access at home (binary: yes or no)
23 romantic - with a romantic relationship (binary: yes or no)
24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29 health - current health status (numeric: from 1 - very bad to 5 - very good)
30 absences - number of school absences (numeric: from 0 to 93)

# these grades are related with the course subject, Math or Portuguese:
31 G1 - first period grade (numeric: from 0 to 20)
31 G2 - second period grade (numeric: from 0 to 20)
32 G3 - final grade (numeric: from 0 to 20, output target)

Acknowledgements

If you use this dataset in your research, please credit the authors

Citations

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

--- Original source retains full ownership of the source dataset ---

DWR implemented the 2011 Georgiana Slough NonPhysical Barrier (GSNPB) Study to test the effectiveness of using a non-physical barrier, referred to as a behavioral Bio-Acoustic Fish Fence (BAFF), that combines three stimuli to deter juvenile Chinook salmon from entering Georgiana Slough: sound, high-intensity modulated light (previously known as stroboscopic light), and a bubble curtain. This report presents the results of the experimental tests conducted in 2011.

Analysis of ‘Body performance Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kukuroo3/body-performance-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is data that confirmed the grade of performance with age and some exercise performance data.

Content

data shape : (13393, 12)

• age : 20 ~64
• gender : F,M
• height_cm : (If you want to convert to feet, divide by 30.48)
• weight_kg
• body fat_%
• diastolic : diastolic blood pressure (min)
• systolic : systolic blood pressure (min)
• gripForce
• sit and bend forward_cm
• sit-ups counts
• broad jump_cm
• class : A,B,C,D ( A: best) / stratified

Source

link (Korea Sports Promotion Foundation) Some post-processing and filtering has done from the raw data.

--- Original source retains full ownership of the source dataset ---

Indonesia Guarantee Institution: Financial Performance: Income: Non Operational Income data was reported at 2.295 IDR bn in Jan 2025. This records a decrease from the previous number of 28.922 IDR bn for Dec 2024. Indonesia Guarantee Institution: Financial Performance: Income: Non Operational Income data is updated monthly, averaging 19.000 IDR bn from Nov 2016 (Median) to Jan 2025, with 99 observations. The data reached an all-time high of 513.888 IDR bn in Dec 2019 and a record low of -453.915 IDR bn in Nov 2020. Indonesia Guarantee Institution: Financial Performance: Income: Non Operational Income data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI017: Financial System Statistics: Guarantee Institution Sector.

Data is split by the following product lines: - Assistance - Credit & Surety - General Liability - Income Protection - Legal Expenses - Marine, Aviation & Transport - Medical Expenses - Motor - Property - Workers Compensation - Misc. Financial Loss

Data on each company and product line includes: - GWP - NEP - Amount reinsured - Net claims - Operating expenses - Loss ratio - Expense ratio - COR

Analysis of ‘ONC Budget Performance Measure Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b798c1fb-8b7a-4f25-9180-0b593959d82f on 11 February 2022.

--- Dataset description provided by original source is as follows ---

The dataset contains all the current and historical performance measures submitted as part of ONC;s annual budget formulation process. These measures track agency priorities for electronic health record adoption, health information exchange, patient engagement, and privacy and security. Each measure contains the annual estimate and a measure target, if applicable, for all the years the measure was reported in the ONC Budget.

--- Original source retains full ownership of the source dataset ---

DWR implemented a large-scale experimental testing program in 2011 and 2012 to assess the effectiveness of a non-physical barrier as a method for guiding downstream migrating juvenile salmonids. The experimental design of the 2011 and 2012 tests included the use of acoustically tagged juvenile late fall-run Chinook salmon (2011 and 2012) and steelhead (2012), released upstream of the nonphysical barrier when the barrier was on and when it was off, to determine the effectiveness of the barrier. This report presents the results of the experimental tests conducted in 2012 with additional discussion of the results of tests conducted at Georgiana Slough in spring 2011.

Indonesia Corporate Sector: Number of Listed Non Financial Corporation for Performance Ratio Calculation data was reported at 432.000 Unit in Jun 2023. This records a decrease from the previous number of 634.000 Unit for Mar 2023. Indonesia Corporate Sector: Number of Listed Non Financial Corporation for Performance Ratio Calculation data is updated quarterly, averaging 423.500 Unit from Mar 2013 (Median) to Jun 2023, with 42 observations. The data reached an all-time high of 648.000 Unit in Dec 2022 and a record low of 107.000 Unit in Jun 2015. Indonesia Corporate Sector: Number of Listed Non Financial Corporation for Performance Ratio Calculation data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI022: Financial System Statistics: Corporate Sector.

This dataset comprises of the data presented in the publication "Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing" (He et al., 2021) and the codes for data analyses.Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:He, X., Liu, W., Qin, N., Lyu, L., Dong, X., & Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.

Publication of information on Non-consolidated Performance Related Pay data for departments and their agencies from performance year 2010-11

Previous publications can be found via the link below:

http://data.gov.uk/dataset/performance-related-pay-defra

Abstract Purpose: The purpose of this study is to analyze the relationship between family influence, measured through power, experience and culture (F-PEC) and family business (FB) performance. Performance is measured from a financial and non-financial perspective. Design/methodology/approach: Empirical study using the quantitative method and data collected through a questionnaire, answered by 169 Portuguese family firms. The survey design was based on prior research of FB performance and the F-PEC questionnaire. The exploratory factor analysis and multiple linear regression models are used. Findings: The results indicate a negative relationship between experience and financial performance, a positive association between a culture of family commitment and performance (financial and non-economic goals), and a positive relationship between a culture of family values and non-economic goals. The results show the importance of agreement between the firm and the family goals. Family influence on FB performance cannot be seen only from a positive (stewardship theory) or a negative (agency theory) perspective. Originality/value: Commitment increases financial performance and the achievement of non-economic goals (perpetuity and family assets). It is important to study how a culture of commitment leads to superior performance.

The dataset contains Year Wise Performance of Ombudsmen at Different Centres - Non Life Insurance from Handbook on Indian Insurance Statistics