zilongwang/ReadingBank huggingface dataset:
Messaging system is complex, consuming more hardware and networking resources Voice messaging is expensive, not integrated with directory infrastructure, and requires maintenance in many places High mobile solution cost, management and security inhibit broader deployment Messaging – Maturity Model BASIC STANDARDIZED RATIONALIZED DYNAMIC Basic e-mail capabilities via minimal or decentralized IT support No shared calendar or contacts E-mail and calendar solution with basic AV/AS/anti-phishing protection and disaster recovery E-mail platform support setting and managing policies at the organizational level to meet your organization’s compliance requirements Secure remote online and offline access to rich client functionality inside & outside the firewall. E-mail and calendar solution provides protection with business continuity to minimize downtime and data loss E-mail platform supports advanced compliance capabilities that facilitate archiving, journaling, encryption, policy enforcement at the user level Mobile device access to e-mail, calendar, contacts available to more than 50% of the information workers Federation of communication information [i.e. e-mail, calendar, presence] and policy [i.e. Information Rights Management, compliance] & Typical customer pain includes: Time is wasted trying to find availability of people and right methods to communicate Increasing security and compliance requirements across all communications methods can lead to disjointed solutions Public IM growth among employees increases security risks Employees need flexibility to be productive at work, home, and especially while traveling Disparate communications tools are often not integrated with Outlook and Office, where information workers spend much of their time IM & Presence – Maturity Model STANDARDIZED RATIONALIZED DYNAMIC Public IM, ad-hoc use for business Secure managed enterprise wide IM infrastructure leveraging unified directory Rich integrated presence enabled Desktop Productivity applications Rich integrated presence-enabled LOB applications Integration across UC, extend to business processes & LOB apps
Solar System Data Sheet Worksheet 1. d 2. A, E (8 choices) 3. H (8 choices) 4. F (8 choices) 5. D (8 choices) 6. B (8 choices) 7. C, G (8 choices) 8. b 9. a 10. b 11. b 12. a, c 13. c Scoring Guide 13 – 4 11-12 – 3.5 9-10 – 3 7-8 – 2.5 6 – 2 5 – 1.5 4 – 1 1-3 – .5 0 – 0
DATA SHEET Features Fast, reliable EMI/RFI testing and verification, while providing 100% effective shielding of 100dB@2.4GHz and 90dB@5.5GHz attenuation as required by EN50147-1 IEE299 High performance construction of galvanized SAE1020 steel and copper EMC shielding with ROHs compliant welding and RAL 9010 STD coating. User Friendly Operation with Front Loading, Swing Away Double Electric Contact door (DEC) with internal parallel, toggle, latch locking action. Shielding tested under lab conditions with signal generator by spectrum analyser and conforming antenna. Manufacturer’s C.O.T. and test results included. Table top model with mounted in-line rack. Compact, small footprint & portable with immaculate body finish. Custom-made product adaptable to any application. Optional Accessories Anechoic absorber lining for the interior surface Data adapter kit with penetration panel Filtered D Type filtered RJ45, filtered USB, SMA etc connectors. Power and signal line filters Observation window Internal illumination Fiberplex waveguide filter for fiber optics Grounding Point Honey-comb air vents and exhaust fan Additional in-port/out-port systems Extra custom-made requirements Dimensions Table Top Model A: 500mm X 500mm X 500mm Model B (medium size): 900mm X 900mm X 1200mm Upright Model: 1800mm X 1100mm X 900mm Applications It is perfect for Wi-Fi, Bluetooth, RFID, 3G & WLAN testing, GPS and Wimax, and may also be used for cell-site to cell-site testing, burst electronics applications, RF resistor testing, two-way radio testing, and susceptibility testing. This product is designed to meet a variety of demanding testing, QC/QA, and repair requirements calling for shielding performance through to It may be used in any environment where the equipment under test needs high performance RF shielding, while isolating it from the operator.
University courses Specialised data analytics courses and subjects offered by tertiary institutions across Australia will help the APS improve its technical data analytics capability, while boosting the number of future graduates with the required data skills and capabilities. The following courses and individual subjects offer opportunities for development in specialised data skill sets, such as maths, statistics and social research methods, and the use of data analytics tools. Many degrees offer the opportunity to complete specialised electives, or to apply the skills to real-world problem or final project. APS employees studying at tertiary institutions further facilitate the close collaborative partnership between the APS and the research and academic sectors to find real-world solutions to policy problems. Candidates These courses are open to the general public and APS employees. APS employees working in areas that undertake technical data analytics work, as well as those working in programme implementation and policy development, will benefit from tertiary level courses. APS agencies will determine their data analytics capability needs and the level of participation in these courses based on individual circumstances. Costs Individual agencies are responsible for covering any tuition or study costs associated with these subjects/courses. Employees should contact their Learning and Development team within their Human Resources area to discuss study options and financial assistance. Study Assistance schemes encourage and support employees to undertake tertiary study that will contribute to the improvement of their professional skills and knowledge. Timeframes Most courses can be completed part time or through online or evening classes. Employees should seek the support of their manager and their Human Resources area and discuss the implications for their work priorities and workload. Short courses and professional development opportunities Australian National University Participants can in these courses on a non-award basis. Please visit to apply.
recommends a conditional admission, the program liaison will place the attribute on the applicant record. The applicant will receive a decision code of probation. The students will be reviewed after the first semester to determine if the student met continuation standards. Legacy as classification code CD and Action Code Probation should migrate with the COND attribute. DEXI Domestic Exchange (IN) DEXO Domestic Exchange (OUT) DHON Departmental Honors FATD Free Audit, Totally Disabled IEXI International Exchange (IN) IEXO International Exchange (OUT) NOL Not Online OFCC Off Campus Contracts Continuing Education, Graduate School, and Records This is used to identify students enrolled or allowed to enroll in contract programs. Those courses/CRNs will be set to only allow people with this attribute to register and will affect the fee structure for the student/course. OSD Office Students w/Disabilities PB2D Post Bacc 2nd Degree UG Admissions This is entered by the admissions staff to indicate a student is working on a second baccalaureate degree program. PCYT Pre Cytotechnology PDNH Pre Dental Hygiene PDNT Pre Dent PLAW Pre Law PMD Pre Med PMDT Pre Med Tech PPHA Pre Pharmacy PPT Pre Physical Therapy PREX Pre-Admit Expired GR Admissions This will be entered by the graduate school liaisons to indicate if a All students converted from classification
Database Instance Chart (new in italics) DB Instance Name Description What Data Who Uses (mainly) Life Cycle CBANSE (SE) Seed SGHE, not confidential DBA Keep CBANTR (TR) Training SGHE plus from training, not confidential All in Project Remove at end of Project CBANCN (CN) Conversion Converted data, confidential IT Refreshed for conversion testing as needed; Remove at end of project CBANPP (PP) Pre Production Validation and rules ready authorized for initial production Functional leads Removed after going live. CBANDV (DV) Development Test data originally from PP and includes converted data, confidential All in Project and Mocks. Negotiated Refresh (by copy of QA). Remove at end of project CBANQA (QA) Quality Assurance PP data merged with converted data before Go Live. After Go Live, copy from PR weekly. All in Project Keep. Refresh weekly by Noon Monday from 7pm Sunday. CBANPR (PR) Production Initially from PP and converted data at point of Go Live. All Keep. Data conversions added when final on project schedule.
Multiple-Choice Record the best or most correct answer on the recording sheet. What word is closest in meaning “unaffiliated” as used in the data? Non-committed Faithful related associated Why is a bar graph appropriate for the religious composition data? It increasing population. It shows a comparison between different religions. It shows change over time. It shows different races. How is “Canada’s Religious Composition” organized? By size of population By changes over time By religion By importance of religion What was true the Catholic faith in 1971? It had decreased. It had fewer members than Protestant faiths. It had more members than other religious faiths. It was a relatively new faith. What is an accurate conclusion that can be drawn from the information in the “Immigrants in Canada” bar graph? Protestant faiths have remained stable during 1971-2011. Religious minorities have increased in size while Christian faiths decreased. More immigrants have lost their faith. There have been more Catholic immigrants in recent years. What conclusion can be supported by the information in this selection? There is a greater increase in people who are religiously unaffiliated. Protestant faiths depend on new immigrants to maintain their membership. There has been an increase in Catholic immigrants in recent years. New immigrants are more than likely atheist. Short Writing Task (Answer in full and correctly written sentences). Identify three aspects that you have seen in the religious landscape in Canada. Use specific examples to illustrate your understanding of this change.
TEENAGERS & GOD Multiple-Choice Record the best or most correct answer on the recording sheet. What word is closest in meaning “ambivalence”? hatred uncertainty fear passion Why is a circle graph appropriate for the data? It shows a process. It shows sequential information. It shows an individual part in relation to the whole. It shows larger components. How is “Teenage Attendance in Church” organized? By size of population By changes over time By religion By frequency of attendance What was true about teenagers in 1984? They were more inclined to be atheists. They were more inclined to acknowledge a higher power. A majority attended Church. A minority attended Church.
Table 12A. Predoctoral Trainees Supported by This Training Grant (Renewal/Revision Applications and Continuation Progress Reports Only) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Predoctoral (and MSTP) Trainees (Listed Sequentially by Entering Class) Enter Source(s) of Support and Academic Year for Each Grant Year Trainee, Year of Entry, Prior Degree & Institution (Mentor – Department / Program) Grant Year -01 _____ Year -02 _____ Grant Year -03 _____ Grant Year -04 _____ Grant Year -05 _____ Grant Year -06 _____ Grant Year -07 _____ Grant Year -08 _____ Grant Year -09 _____ Grant Year -10 _____ Title of Research Project or Research Topic Degree(s) Received (Year) Current Position and Institution Support Obtained) Program Statistics Percentage of T32 Appointees Entering Graduate School 10 Years Ago That Completed Ph.D.s Average Time to Ph.D. for T32 Appointees in the Last 10 Years (not including leaves of absence)
Table 1. Membership of Participating Departments and Programs (Renewal/Revision Applications) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Participating Department or Program Faculty Members In Department or Program Faculty Members Participating in This Application Predoctoral Trainees in Department or Program [Supported by Any NIH Training Grant] Predoctoral Trainees With Participating Faculty Total (TGE) A/B/C Predoctoral Trainees Supported by This Training Grant Total (TGE) A/B/C Postdoctoral Trainees in Department or Program [Supported by Any NIH Training Grant] Postdoctoral Trainees With Participating Faculty Total (TGE) A/B/C Postdoctoral Trainees Supported by This Training (TGE) A/B/C Totals Need not sum Need not sum
Table 5A. Predoctoral Trainees of Participating Faculty Members (Alphabetically by Faculty Member for the Past Ten Years) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Faculty Member Past / Current Trainee Trainee Name Training Occurred) Training Period (Degree) Prior Academic Degree Prior Academic Degree(s) Prior Academic Degree Year(s) Title of Research Project Current Position of Past Trainees / Source of Support of Current Trainees
Table 6A. Publications of Research Completed by Predoctoral Trainees (New Applications) (Group Past and Current Trainees Separately, then sort by Year of Entry) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Mentor(s) Past / Current Name of Trainee in Program) Publication (Authors, Year, Title, Journal)
Table 7B. Admissions and Completion Records for the Participating Departments and Programs During the Past Five Years (Postdoctoral Applicants) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Department / Program Entering Year Previous Degree Type Applicants Applied (TGE) A Applicants Accepted (TGE) A Applicants Entered Program (TGE) A/B/C Trainees Still in Program (TGE) A/B/C Trainees Completed Program (TGE) A/B/C Trainees Left Program (TGE) A/B/C Reason for Leaving Program (if training was not completed) All Programs Sums Averages
Definitions Work-related asthma: Respondent answered “yes” to any question of 1 through 7. Told to or by a doctor or health professional that asthma was related to work: Respondent answered “yes” to either question 1 or 2. A person’s self-assessment that asthma is related to his/her work: Respondent answered “yes” to any question from 3 through 6. Work-related asthma symptoms severe enough to change or quit a job: Respondent answered “yes” to question 7.
Totals Need not sum Need not sum Table 2. Participating Faculty Members (Alphabetically by Faculty Member) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Name/Degree(s) Rank Primary (& Secondary) Appointment(s) Role in Program Research Interest 3. Institutional Training Grant Support Available to Participating Faculty Members, Department(s), or Program(s) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Title of Training Grant Funding Source Including Identifying Number Active or Pending Project Period Program Director (Department) Predoctoral Trainees Supported This Year Postdoctoral Trainees Supported This Year Trainees Supported This Year Total No. of Participating Faculty (Number Overlapping) Names of Overlapping Faculty
7A. Admissions and Completion Records for the Participating Departments and Programs During the Past Five Years (Predoctoral Applicants) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Department / Program Entering Year Applicants Applied (TGE) A Applicants Accepted (TGE) A Applicants Enrolled (TGE) A/B/C Trainees in Program (TGE) A/B/C Trainees Completed Program Earned PhD or MD/PhD (TGE) A/B/C Trainees Left Program Earned Other Degree (TGE) A/B/C Trainees Left Program Without Degree (TGE) A/B/C Reason for Leaving Program (if training was not completed) Total All Programs Sums all Years Average all
Table 8A. Qualifications of Recent Predoctoral Applicants OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Year Department / Program Applicant (List by Number) Previous Institution(s) Degree(s) & Year(s) Undergrad GPA GRE Scores V, Q, A, S (Percentiles) and/or MCAT Scores Interviewed (Y/N) Accepted (Y/N) Enrolled (Y/N) Support from this Grant (Y/N) Program Statistics Total Number of Applicants Number of TGE Applicants Applicants Interviewed Applicants Accepted Applicants Enrolled Applicants Supported By This Average GRE and/or MCAT Scores Average GPA Table 9A. Qualifications of the Current Predoctoral Trainees Clearly Associated with the Training Program (New Applications) OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018)
Department / Program Trainee (List by Number) Previous Institution(s) Degree(s) & Year(s) Undergraduate GPA GRE Scores V, Q, A, S (Percentiles) and/or MCAT Scores Current Research Mentor in Program Column Blank Program Statistics Total Number of Trainees Number of TGE Trainees Average GPA Average GRE / MCAT Scores Table 10: Admissions and Completion Records for Underrepresented Minority (URM) Trainees, Trainees with Disabilities, and Trainees from Disadvantaged Backgrounds Clearly Associated With the Training Program OMB Number 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018) Diversity Recruitment Group Trainee (List by Number) Entering Year (Pre/Post) Department / Program Source of Support and if Support by NRSA Grant In Training Completed Training Left Without Completing Training Current Status Career or Employment
6. Date for the organizational meeting: ___________________________________________________________ approximately 8 weeks in advance of the date of the event is recommended 7. Estimated number of teams and number of students per team 8. List of potential mentors and potential judges. Please include name, position, and institution/organization of each person listed. We suggest two or three mentors per student team, and between three and five judges, although this will depend as well on local circumstances Name Position Institution Confirmed (C) or Tentative (T)
Data Validation Checks S.No. Item Code Explanation 1 A1339 = A1600 A1339:Profit/Loss of Current Year A1600:Net Profit/ Loss from Continuing and Discontinuing Operations 2 A1100 = A1300 A1100 : Equity and Liabilities A1300 :Total Assets 3 A1402 = A2150 A1402: Other Income(Under Revenue from Operation Head) A2150: Total Other Income 4 A2067 = A1450 A2067: Total Finance Cost Calculated A1450: Finance Cost Charged to P&L account 5 A3080 = Sum of All States no. of Employee A3080: Total number of employees 6 = A3100+A3130 A3080: Total number of employees A3100 : Sum of total number of employees in Category IDA A3130 : Sum of total number of employees in Category CDA 7 A5020 = A1409 A5020: Salary Wages A1409:Salary, Wages & Benefits/Employees Expenses 8 A4020 = A1409 A4020: Charged to P&L account A1409:Salary, Wages & Benefits/Employees Expenses 9 A1210+1240+1245=Sum of All States no. of Fixed asstes(Gross Block) Assets A1240:Capital work-in-progress A1245:Intangible Assets
Tentative Schedule (Changes to be announced) Approx. Week & date Topic Internal External Week 1, 01/22 Introductions and overview of the course KJ Week 2, 01/29 DW overview - Cornel & Rob KJ Week 3, 02/05 Ch 1 & 2 KJ Week 4, 02/12 Ch 9 KJ Week 5, 02/19 Ch 10 KJ Week 6, 02/26 Ch 11 KJ Week 7, 03/05 Guest Topic + Ch 3 (Partly) Guest Speaker Week 8, 03/12 Ch 3 (Balance) John Powell Week 9, 03/19 Ch 5 Ch 6 Yanhui Long Jenny Brunnert Week 10, 03/26 Spring Break – no classes Week 12, 04/02 Ch 7 Ch 18 Elaine Love Sean Isserman Week 13, 04/09 Ch 18 (any overflow if needed) Ch 8 Sean Isserman Geoff Hickman Week 14, 04/16 Ch 12 Rudy Kardjaja Week 15, 04/23 Ch 13 Moying Zhou Week 16, 04/30 Ch 15 Daniel Diecker Week 17, 05/07 Review Week 18, 05/14 Exam 1 in class 7:45 PM to 9:45 PM
Report of staff that report feeling competent to deliver suicide care Percent of staff that report feeling confident to deliver suicide care Total # of staff that report feeling competent to deliver SC/ Total # of staff Number of that report feeling confident to deliver SC/ Total # of staff 3) Universal Identification of those at risk for suicide The percentage of patients receiving “universal” suicide risk screening The percentage of patients receiving positive suicide risk screening referred for assessment Number of patients who received a suicidescreeningduring the reporting period/ Number of patients enrolled Number of patients who received a suicidescreeningduring the reporting period/ Number of patients enrolled during the reporting period during the reporting period 4) Collaborative engagement of at risk for suicide care The percentage of patients with a safety plan Percentage of patients with a counseled for resection of lethal means Number of clients with a safety plan developed (same day as screening) during the reporting period/ Number of clients who screened and assessed positive for suicide risk duringthereportingperiod Number of clients who screened and assessed positive for suicide riskandwerecounseledabout lethal means (same day as screening) during the reporting period/ Number of clients who screened and assessed positive for suicide risk duringthereportingperiod 5) Evidence culturally informed treatment provided to all at risk for suicide Percentage of staff utilizing EBP to provide treatment of suicide risk Percentage of staff incorporating culturally- informed practices and Total number of utilizing EBP to provide treatment of suicide risk/ Total number of staff Total number of staff incorporating culturally-informed practices and
Data Collection Week of Data Collection: ELP Standard By the end of each English language proficiency level, an ELL can … 1 Level 2 Level 3 Level 4 Level 5 4-5. 2 An ELL can . . . participate in grade appropriate oral and written exchanges of information, ideas, and analyses, responding to peer, audience, or reader comments and questions. participate in conversations participate in short written exchanges actively listen to others respond to simple questions and some wh questions about familiar topics. participate in conversations participate in short written exchanges actively listen to others respond to simple questions and wh questions familiar topics and texts. participate in short conversations and discussions participate in written exchanges respond to others’ comments add some comments of his or her own ask and answer questions about familiar topics and texts. participate in conversations and discussions participate in written exchanges build on the ideas of others express his or her own ideas ask and answer relevant questions add relevant information and evidence about a variety of topics and texts. participate in extended conversations and discussions participate in extended written exchanges build on the ideas of others express his or her own ideas clearly pose and respond to relevant questions add relevant and detailed information evidence summarize the key ideas expressed about a variety of topics and texts.
1..3 medium::type [coastline, waterColumn and seabed] 1..1 litterAmount::real Macro-litter by number of items, typified by matrix and type. Decision (EU) 2017/848) D10C2!^^ in coastline, water column and seabed 1..n litter::type 1..3 medium::type [coastline, waterColumn and seabed] 1..1 litterAmount::real by number of items, typified by matrix and type. Litter categories (as listed in COM Decision (EU) 2017/848) Point OR Grid OF RSCs+++ / EMODnet/ ICES Distribution of impulsive noise events per year 1..1 noiseEvent::Integer event to be defined] Number of events occurring. Grid or Polygon SR or OF RSCs+++ - ICES D11C2 Distribution of continuous low-frequency sound level 1..1 ContinuousNoise::boolean Presence or absence of continuous LFSL. Grid or Polygon SR or OF RSCs+++ D1C1! By-catch distribution 1..n species::type 1..1 For each grid cell or polygon there’s a by-catch distribution associated to a species distribution. Species assessed Grid or Polygon SD RSCs (HELCOM) +++ D1C4 Species distribution 1..n species::type 1..1 SpeciesNumber::real Each grid cell contains the number of individuals of a particular species. Species assessed Grid or Polygon SD RSCs++++ / EMODnet D1C6 Habitat distribution 1..n habitat::type 1..1 habitatCoverage::real Each grid cell or polygon contains the habitat coverage (km2), typified by habitat type. Habitats assessed Grid or Polygon HB RSCs+++ / EMODnet D6C4^ Distribution of habitat loss 1..n habitat::type 1..1 habitatLoss::real [reference to habitat baseline] Habitats assessed Grid or Polygon HB
Ms. Valenti’s Class Quiz Scores Tally Frequency 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 Use the data for Ms. Valenti’s class quiz scores to complete the frequency table and the line plot Ms. Valenti’s Class Quiz Scores 20.5 24 20.5 19.5 18 20 19.5 20.5 21 23 20 23.5 16 15 15.5 21.5 22.5 19 22 17.5 16 19 16.5 Assignment continues on the next page!!!!
Ms. Class Quiz Scores 15 24 24.5 15.5 22 23.5 15.5 17 15 15.5 24 22.5 22 22 23 16 16.5 17 24.5 17 15.5 16 24 Ms. Milia’s Class Quiz Scores Score Frequency 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 Data About Us Investigation 1.2 Homework-Describing the Data Using Summary Statistics
Deaths data ONS identifiable level data - HSCIC append some data (GP/GP practice codes). Identifiers are: Address Postcode of usual residence of the deceased, post code of place of death NHS number DoB DoD Maiden name Name of certifier Name of coroner Cause of death basis for access to PCMD: Section 42(4) of the SRSA (2007) as amended by section 287 of the Health and Social Care Act (2012) and Regulation 3 of the Health Service (Control of Patient Information) Regulations 2002 Legal basis for dissemination of the data by HSCIC is Health and Social Care Act s261 5(d) the disclosure is made to any person in circumstances where it is necessary or expedient for the person to have the information for the purpose of exercising functions of that person conferred under or by virtue of any provision of this or any other Act. Regulation 3 provides the detail of that function conferred. Users are able to view data for 2006 to 2015. Data for 2016 is available up until when the last monthly update was carried out. Approved users are able to view and download data on the PCMD system related to their LA only (this includes CCGs for their LA only). Downloaded data that is no longer needed will be deleted on the basis that it is still available from the PCMD if the need for it re-occurs. Births Data ONS identifiable level data. Identifiers are: Address of residence of mother Place of birth text Postcode of usual residence of the mother, post code of place of birth of child NHS number of child DoB of child Legal basis for access to PCMD: Section 42(4) of the SRSA (2007) as amended by section 287 of the Health and Social Care Act (2012) and Regulation 3 of the Health Service (Control of Patient Information) Regulations 2002. Legal basis for dissemination by HSCIC: Health and Social Care Act s261 (5d). Local authority to insert what data they currently hold Approved users receive data via secure email related to their LA only (this includes CCGs for their LA only). Vital Statistics tables ONS aggregated level data Local authority to insert what data they currently hold Approved users receive data via secure email.
Strengths Shows parts of a whole. Shows percents of the total. Sizes of sectors compare parts of the whole. Limitations Does not show data values and the Difficult to draw accurately. _________________________ Strengths Lengths of bars compare data values. Scale can be used to find the total. Easy to draw. Limitations May be difficult to read depending on scale used. Does not show percents of the total for comparison. ________________________ Strengths Easy to draw and to read. Shows data changes over time. Can be used to estimate values between or beyond data points. Limitations Does not show of a whole. Zig-zag pattern can be difficult to interpret. ________________________ Strengths Lengths of rows of symbols compare data values. Graph is visually appealing Key can be used to find the Limitations number of symbols makes it difficult to read. Does not show parts of a whole. Difficult to draw. _________________________ Strengths Directly compares two sets of data. Lengths of bars compare data values. Scales can be used to find the total of each data set. Easy to draw. Limitations Can only be used to show discrete data. May be difficult to read, depending on the scale used. Two sets of data in one can be confusing.
DATA AUDIT Review all your databases, email lists, spreadsheets, paper documents and other lists of personal information. If there are any issues, identify what you need to do. If action is not clear, then highlight questions needing further input. New consent forms, privacy notices, and new or revised policies or procedures are likely to be needed to ensure compliance with GDPR. Refer to the Law Department’s guidance on the Church website for further information. Description Why is the data held and what is it used for Who holds the data and who can access it? What security controls are in place? How long is data kept for? Is the data updated? Is the data disclosed to anyone? Is this covered by our privacy notice? ACTION REQUIRED Example: Gift Aid Declarations For claiming Gift Aid Held by Gift Aid Officer. Also accessed by treasurer On paper, kept in a locked filing cabinet Six complete calendar years after last gift claimed on the declaration No No No – not yet written a privacy notice Write privacy notice
Document control sheet HRB – Data Breach Protocol Title: Data Breach Protocol Document Type: Protocol Last Updated: 23.05.18 Background: This policy applies to all HRB employees, service providers, contractors, Peer Reviewers, interns, agency workers and other persons engaged in activities relating to HRB on a permanent or temporary basis (hereinafter referred to as “Data Users”).
College and Career Descriptors) Expectations 1 Stage 2 Stage 3 ACADEMIC DISCOURSE (ACADEMIC CONVERSATION AND WRITING) The teacher structures opportunities for students to engage in academic discourse and teaches students facilitation skills to enhance own conversations Teacher is the primary driver of the conversation; most exchanges are teacher to student or student to teacher Teacher initiates the conversation; students begin to engage in pairs, small group etc. Students initiate and drive the conversation; teacher may use students’ thinking to propel discussions Instruction is structured so that few students are actively engaged and contributing Instruction is structured so some or most students are actively engaged and contributing Instruction is structured so all students are actively engaged and contributing Teacher explicitly teaches and uses scaffolds (e.g., sentence frames, discussion protocols) to language and behaviors for academic conversations (active listening, questioning, clarifying, negotiating, etc.) Students rely on scaffolds and use is stilted Teacher coaches students on how to use academic language and participate in discussions conversations (active listening, questioning, clarifying, negotiating, etc.) Students use scaffolds as needed Students fluidly participate in conversations using internalized protocols for academic language and behavior conversations (active listening, questioning, clarifying, negotiating, etc.) Students construct arguments and share them with peers, but rarely engage each other’s ideas; students may use evidence (e.g., explanations, diagrams, graphs, tables, examples, texts, etc.) to support an idea Students challenge and ask for clarification from their classmates and use evidence (e.g., explanations, diagrams, graphs, tables, examples texts, etc.) to support an idea Students critique each other’s thinking and negotiate understanding using evidence (e.g., explanations, diagrams, graphs, tables, examples texts, etc.) to support an idea. They create new ideas or revise their thinking. Notes
Data Collection Framework Instructions: Use this form as a starting for your development efforts. Customize this form, adding relevant information as appropriate or removing information that does not apply to your situation. Evaluation Question(s) Indicator(s) Data Source(s) Data Collection Method(s) What do you want to know? Why do you want to know it? Is it measurable? How will you use the data? How will you know? What observable measures will you use? From where or from whom will you obtain the information? How will you gather the information? Examples: Were curriculum trainings conducted as planned? What is the impact of teacher curriculum trainings? Examples: The number of curriculum trainings implemented Descriptions of the trainings Participants’ perceived confidence to teach the curriculum Participants’ skills in teaching the curriculum Examples: Training agendas, training registrations Professional development coordinator Participants in the trainings Curriculum coordinator Examples: Document review Telephone interview Questionnaire Classroom observations of teachers [ENTER QUESTION] [ENTER INDICATOR] SOURCE] [ENTER COLLECTION METHOD]
Contact Extension E-mail Report/Data Point Description/Data Source Patrick Brown (425) 235-2352 ext. 5537 Current student and class information, historical FTE, DATAX, and enrollment/load reports SMS Kim Cartland (425) 235-2352 ext. 2094 Enrollment, retention, completion, and wage information for AIR Washington programs Quarterly faculty and program evaluations Internal documentation and SMS. Students complete faculty and program evaluations on a quarterly basis, reports are made using Qualtrics and are made available on SharePoint. Foundation Director (TBD) Alysen Laakso TBD (425) 235-7867 TBD Student scholarship and emergency fund information Internal documentation at The Foundation Mark Johnson (425) 235-2352 ext. 5509 Tuition and fee information Grant funding Budget/expenditures for various programs on campus Financial statements for The Foundation FMS IR Office Please complete the IR Data Request Form: Retention/Persistence Completions Enrollment Trends (Headcount/FTE) Student Achievement Transcripts/Grades Financial Aid Student Demographics Student/Faculty Ratios Faculty/Staff Demographics Historical; pulled from state board provided data files. Quarter files are usually available 1-2 months after the end of the quarter. IR Office Please complete the IR Data Request Form: Estimated Placement and Wage Data Historical; provided by the state board. Placement data is released
5.2 A sample Diversity Data Policy is included in Annex A to this guidance. Please note that use of this policy does not guarantee compliance by chambers with their legal and regulatory requirements, for which they remain fully responsible. Chambers’ should review and amend the sample Diversity Data Policy to ensure that it is suitable, having regard to their individual circumstances. 6. Collection of Diversity Data 6.1 In order lawfully to collect Diversity Data from individuals, chambers must obtain the explicit consent of individuals to the collection and processing of their data before collecting such Diversity Data. This is required because of the rules under the Data Protection Act concerning the processing of sensitive personal data (which includes information on health, ethnicity, religion or belief and sexual orientation). All Diversity Data, of whatever nature, should be treated as if it were sensitive personal data for the purposes of compliance with the Code. 6.2 To demonstrate that explicit consent has been obtained, it is necessary that chambers can show that individuals are prominently informed about how their personal data will be used and that the provision of their personal data is voluntary. Likewise, individuals should be told that by providing their Diversity Data they are explicitly consenting to the collection and use of their personal data for the purposes for which they are notified. To this effect, they must be provided with a copy of the written Diversity Data policy when they are invited to provide their Diversity Data. 6.3 Chambers must ensure that consent to the collection and use of the Diversity Data is freely given, and it is the responsibility of all Heads of Chambers and Diversity Data Officers to ensure that it is made clear to all individuals from whom Diversity Data is requested, that there is no obligation to respond and that the provision of Diversity Data is entirely voluntary. 6.4 In particular, chambers must therefore ensure that are no internal procedures or behaviours which could reasonably be considered to compel individuals to provide their Diversity Data. For instance, the provision of Diversity Data should be obtained through a separate mechanism from any other collection of information from members of the workforce. This should mean that a separate form or questionnaire and separate notification is used. The request for Diversity Data should be made in isolation from any other requests for information and, in particular, should in no way be linked or associated with any appraisal or performance review of barristers/members of staff.
Resources for RCR Template Title: Audit to optimise CT KUB imaging in investigation of renal colic. (2015) Twemlow, Mark; Munjal, Ishaana Data collection example table: Audit patient identifier Date of exam DOB Gender Location of scan request Total no. of slices No. of overscan slices % overscan DLP (mGy*cm) 1 --/-- M/F A&E 300 30 10 350
the baseline that the child’s progress will be measured against. This will then be updated monthly. In the future, we will be looking to update the data management system to permit remote access from the field via mobile/tablet devices, allow multiple sources of data entry, have efficient storage and allow retrieval of data enabling effective reporting across the organization or to share with donors and beneficiaries. However, prior to this, it is necessary to establish suitable data collection tools which incorporate the new intervention areas such as; secondary schools, VTIs, child protection, girls’ mentorship both in primary and secondary schools and teacher mentorship. The data collected will be both qualitative and quantitative and where possible made suitable for collection on a mobile device and standardised so that data is in a format that can be easily uploaded onto a data management system. Objectives There are four main objectives for this consultancy: Objective 1 – Upload existing bio-data onto Data Management System Support LCD-GECT to upload the bio-data onto the Data Management System so that a baseline for each beneficiary is determined Where necessary, update the Data Management System to include bio-data previously not collected Ensure the project has complete datasets on all beneficiaries and that the data meets the required standards and is accurate Objective 2 – Develop new Data Collection Tools and processes Identify the project data necessary to satisfy the requirements of the donor and the monitoring and evaluation framework Develop a full set of simple data collection tools that enables collection of the project data identified and designed to calculate the impact of the project at the local level Ensure the data collected via tools can be easily uploaded onto a simple Data Management System and enables efficient data collection, verification, management and formatting Consider the use of mobile devices/tablets for some data collection when developing data collection tools Data collection tools should incorporate guidelines on how data needs to be collected, stored and processed to ensure data protection Objective 3 – Train project team and partner staff on the use of data collection tools
Data Destruction Form – Schedule 1: Lot Information Client Name Client Address Client # Shared Services Tag U of M Capital Asset ID # Make Model Media Serial Number System Serial Number Provide all available information. The media serial number is only required if the media is not securely installed in a system.
Data dictionary for mort_hist_summ _1948–2015_data_file.csv Type: Comma separated values file (.csv) Contains: Mortality data for certain causes of death by sex from 1948–2015. Mortality data for and non-Māori for certain causes of death by sex from 1996–2015. Data sources: Data from 1948–1995 was sourced from publications in the Ministry of Health . Data from 1996–2015 was sourced from the New Zealand Mortality Collection electronic records, extracted on 13 February 2018. Variable Description year Death registration year causeofdeath Underlying cause of death (grouped) - refer to the coding information in Mortality: Historical summary 1948–2015 for how the ICD codes are grouped over time sex Sex of person at death (Male, Female, AllSex) ethgroup Ethnic group (AllEth, Māori, non-Māori). Uses prioritised ethnicity, by which each person represented in the data is allocated to a single ethnic group using the priority system Māori > Pacific peoples > Asian > European or Other. The non-Māori ethnic group aggregates all people allocated a Pacific, Asian, or European or Other ethnicity. number* Number of registered deaths asrate* Age-standardised rate per 100,000 (refer to definition below) *Unavailable data ‘…’ in the number or asrate column of the .csv file indicates that data was unavailable for that variable. This applies to data for: other forms of heart disease data (unavailable prior to 1950) chronic lower respiratory disease data (unavailable prior to 1968). Age-standardised The rates in this file have been age standardised to take into account the differences in the age distribution of the population over time. We used direct age-standardisation and the WHO World Population as the standard population. For more information about the reference population refer to .
3.14 Amount Attribute Attribute Description Field Name Amount Description The cost incurred for the hospitality in question. Obligation Mandatory Format Type Numerical – Note, do not include dollar signs ($), all amounts include taxes. Validation This field must not be empty 3.15 Additional Comments (English) Attribute Attribute Description Field Name Additional Comments (English) Description This field may be populated with additional comments in English. Obligation Optional Format Type Free text 3.16 Additional Comments (French) Attribute Attribute Description Name Additional Comments (French) Description This may be populated with additional comments in French. Obligation Optional Format Type Free Text
3.10 Destination (English) Attribute Attribute Description Field Name Destination (English) Description To include names of all places visited to conduct government business during the trip in question, in English Obligation Mandatory Format Type Free text – Note, use full city/region, province/state and country name where possible (i.e.: Ottawa, Ontario, Canada or New York City, New York, United States) Validation This field must not be empty 3.11 Destination (French) Attribute Attribute Description Field Name Destination (French) Description To include of all places visited to conduct government business during the trip in question, in French Obligation Mandatory Format Type Free text – please use full city/region, province/state and country name where possible (i.e.: Ottawa, Ontario, Canada or New York City, New York, United States) Validation This field must not be empty 3.12 Airfare Attribute Attribute Description Field Name Airfare Description Total cost of the airline ticket, if applicable. Obligation Optional Format Type Numerical– Note, do not include dollar signs ($), all amounts include taxes. 3.13 Transportation Attribute Attribute Description Field Name Other Transportation Description cost of all other forms of transportation (e.g. train, bus, vehicle rental, private vehicle, taxis, etc), if applicable. Obligation Optional Format Type Numerical – Note, do not include dollar signs ($), all amounts include taxes. 3.14 Accommodation Attribute Attribute Description Name Accommodation Description Total cost of accommodation. Obligation Mandatory Format Type Numerical – Note, do not include dollar signs ($), all amounts include taxes. Validation This field must not be empty
EndNote essentials Reference data entry Data entry rules For all fields, consistency is the key – if there are multiple references for a particular author, title, keyword, etc., ensure that the data is entered the same way every time. The data will display in red if it does not already exist in the library – this is an indicator that data is being entered inconsistently. Reference type: Journal article is the default reference type. Don’t forget to change the reference type as required. Author Fields: Individuals Names can be entered in multiple formats. Enter the full name wherever possible and enter the name exactly the same each time. EndNote will abbreviate names as required by the output style. John James OR (GU recommendation) Smith, J. J. OR John James Smith OR J. J. Smith Where the full name is not known, separate initials with a space OR a full stop. EndNote will include or remove the punctuation as required by the output style: Smith, J J OR (GU recommendation) Smith, J. J. OR Smith, J.J. Multiple word last names MUST be entered in the following format: de Marquez, Gabriel OR de Marquez, G. Author names that include a generation title should be entered in the following format: Getty, J. Paul, III Getty, J. Paul, Jr Co-authors When a text is co-authored, enter all authors’ full names where known. EndNote will truncate the list with “et al.” or “and others” as required by the output style. Enter each name on a separate line:
Data from 2016 Health Resources and Services Administration (HRSA) UDS Report for WNCCHS Demographic WNCCHS NC Health Centers U.S. Health Centers Number of patients served in 2016 (unduplicated) 15,448 508,599 21,880,295 Total visits in 2016 80,601 1,771,333 104,116,257 Percent of patients uninsured 56.82% 43.18% 32.43% Percent of patients who are racial/ethnic minority 42.89% 66.63% 62.27% Percent of patients at or 200% of Federal Poverty Level 95.05% 92.63% 91.95% Patients with Hypertension 33.99% 33.92% 26.22% Patients with Diabetes 17.41% 19.79% 14.30% Patients with Asthma 5.70% 4.96% 5.47% HIV 6.04% 0.92% 0.72% Clinical Measure WNCCHS NC Health Centers U.S. Health Centers Access to prenatal care in first trimester 87.09% 72.52% 74.06% Weight assessment and counseling for children and adolescents 80.51% 67.34% 62.92% BMI Screening and follow up 82.07% 55.93% 62.46% Tobacco use screening and intervention 96.87% 86.15% 85.19% Colorectal cancer screening 52.97% 39.10% 39.89% Screening for clinical depression and follow up plan 94.58% 58.62% 60.34% Cervical Cancer screening 77.82% 54.17% 54.36% Childhood immunization status 82.61% 54.24% 42.80% Use of appropriate medications for asthma 96.22% 81.72% 87.38%
Clinical Measure WNCCHS NC Health Centers U.S. Health Centers Coronary artery disease (CAD): Lipid Therapy 85.71% 75.01% 79.46% Ischemic Vascular Disease: Use of aspirin or another Antithrombotic 96.64% 80.08% 78.40% Controlling high blood pressure 69.47% 61.31% 62.39%
TABLE OF CONTENTS Policy …………………………………………………………………………….. 3 Scope …………………………………………………………………………..…. 4 Risk Management ……………………………………………………………….... 6 Information Security Definitions …………………………………………………. 6 Information Security Responsibilities ..…………………………………………... 7 Data Classifications ..…………………………………………………………….. 9 Computer and Information Control ……………………………………..………..11 Data Quality ……………………………………………………………..…….....18