Our report addresses: An overview of our conference planning, review, and approval processes; Agency-sponsored and federally sponsored/hosted conferences in excess of $500,000; and Agency-sponsored and federally sponsored/hosted conferences in excess of $100,000. Report for FY 2015.

This statistic depicts the total and professional attendance of the top 50 medical meetings in the United States in 2015. The Chicago Dental Society's Midwinter meeting reported a total attendance of ****** in that year.

The dataset ”DBLP-SIGWEB.zip” is derived from September 17, 2015 snapshot of dblp bibliography database. It contains all publications and authors records (available in dblp data and ACM metadata) of 7 ACM SIGWEB conferences (HT, DL, DocEng, WebSci, CIKM, WSDM, UMAP) dblp-sigweb.sql file creates 15 tables in mysql. Followings are the list and description of all attributes and tables used in the dataset. Same attributes used in different tables are listed only once.

1. Table- papers dblp_key- unique id of each publication in dblp database crossref- unique id of each conference in dblp database doi- unique doi url to publisher page paper_id- unique id of each article in acm digital library (DL) cite_count- number of citations for each article calculated for the papers published in acm DL pages- number of pages for each article in conference proceedings conf_id- unique id of each conference in acm DL funding- funding source information of article. NULL- if no funcding source available

2. Table- paper_authors author_id- unique id of an author in acm DL affiliation- affiliation information of author for associated article

3. Table- concepts concept- concepts in an article- tagged by ACM

4. Table- author_tags author_tag- Keywords/tags provided by authors

5. cited_by paper_id- acm DL id of article A to be cited cite_id- unique id of article that has cited article A

6. paper_references refer_id- unique id of the articles (published in sigweb conferences) cited in article A.

7. Table- conferences dblp_key- unique id of each conference in dblp database year- year of the conference publisher- publisher name of each conference (ACM, Springer, IEEE etc.) title- full name of the conference proceeding doi- unique doi url to the conference publisher page

8. Table- general_chairs, program_chairs, editors author_id- unique id of author affiliation- affiliation of author

9. authors_affiliation_history, colleagues author_id- unique id of author A in ACM DL position- index of affiliation- starts from 0 affiliation- lists all affiliations of an author colleague_id- lists acm IDs of all authors publishing papers in ACM co-authored with A.

10. authors_info author_name- full name of author acquired from ACM publisher page year_first- year of first article publication in ACM year_last- year of recent article publication in ACM pub_count- total number of publciations in ACM DL cite_count- total number of citations mentioned in ACM publciations avg_cite- average number of citation in ACM publications

11. affiliations_info affiliation- name of the affiliation affiliation_type- type of affiliatioin (Industry, Academic Institution) city, state, country- geographical location of affiliation lat, lng- geocodes of affiliation

12. Table- acceptance rate conf_id- acm id of conference dblp_key- dblp id of a conference submitted- #submission received in conf X in year Y accepted- #accepted papers in conf X in year Y rate- acceptance rate of conf X in year Y.

A review of current literature on the occurrence of waterborne pathogens in DW systems. This dataset is not publicly accessible because: I am using published data from a journal not generated by EPA. It can be accessed through the following means: N/A. Format: no unique data has been generated. This dataset is associated with the following publication: Rochelle, P., P. Klonicki, G. DiGiovanni, V. Hill, Y. Akagi, and E. Villegas. Conference Report: The 6th International Symposium on Waterborne Pathogens ISWP 2015. JOURNAL OF THE AMERICAN WATER WORKS ASSOCIATION. American Water Works Association, Denver, CO, USA, 107(10): 24-32, (2015).

This data was used in the TREC 2015 and 2016 total recall track. The goal of the total recall track was to help develop retrieval systems tuned to retrieving ALL relevant information, as opposed to common web search engines where one good answer could be sufficient.

Makrodaten über 438 sächsische Gemeinden zum Stichtag 01.01.2013. Die Originaldaten sind frei verfügbar und stammen von der Arbeitsagentur für Arbeit, dem Statistischen Landesamt Sachsen, dem Sächsischen Innenministerium, dem Vereinsregister e.V. und dem Zensus 2011.

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

This data set includes all public meetings that were posted to the Transparency website by State of Oregon agencies, boards, commissions and ESD's from January 1. 2015 through December 31, 2015. The meetings are sorted by date. For more information about Public Meetings Calendars go to: http://www.oregon.gov/transparency/Pages/PublicMeetingNotices.aspx

Korea TW: International Conference Business: University data was reported at 8,718.000 Person in 2016. This records a decrease from the previous number of 11,096.000 Person for 2015. Korea TW: International Conference Business: University data is updated yearly, averaging 8,108.500 Person from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 11,096.000 Person in 2015 and a record low of 3,874.000 Person in 2007. Korea TW: International Conference Business: University data remains active status in CEIC and is reported by Korea National Tourism Organization. The data is categorized under Global Database’s Korea – Table KR.G045: Employment: Tourism Industry.

Family Group Decision Making (FGDM) is a collaborative approach to service planning and decision making. Using the FGDM approach, families join with relatives, friends, and others in the community to develop a plan to ensure children are cared for and protected from future harm. This broader constellation of “family” convenes with information providers/community supports and CPS caseworkers in a unique partnership that empowers the “family group” with a high degree of decision-making authority and responsibility. FGCs generally are held after a child is removed, but may also be used before removal when the family receives Family Based Safety Services. More information at www.dfps.texas.gov.

Korea TW: International Conference Business: Employee data was reported at 10,031.000 Person in 2016. This records a decrease from the previous number of 13,180.000 Person for 2015. Korea TW: International Conference Business: Employee data is updated yearly, averaging 10,760.500 Person from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 13,180.000 Person in 2015 and a record low of 6,503.000 Person in 2007. Korea TW: International Conference Business: Employee data remains active status in CEIC and is reported by Korea National Tourism Organization. The data is categorized under Global Database’s South Korea – Table KR.G044: Employment: Tourism Industry.

Dataset of the 'Industrial Challenge: Recovering missing information in heating system operating data' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 11th-15th 2015, Madrid, Spain

The task of the competition was to recover (impute) missing information in heating system operation time series'.

Included in zenodo:

• dataset of heating system operational time series with missing values

• additional material and descriptions provided for the competition

The competition was organized by:

M. Friese, A. Fischbach, C. Schlitt, T. Bartz-Beielstein (TH Köln)

The dataset was provided by:

Major German heating systems supplier (S. Moritz)

Industrial Challenge: Recovering missing information in heating system operating data

The Industrial Challenge will be held in the competition session at the Genetic and Evolutionary Computation Conference. It poses difficult real-world problems provided by industry partners from various fields. Highlights of the Industrial Challenge include interesting problem domains, real-world data and realistic quality measurement

Overview

In times of accelerating climate change and rising energy costs, increasing energy efficiency and reducing expenses becomes a high priority goal for businesses and private households alike. Modern heating systems record detailed operating data and report this data to a central system. Here, the operating data can be correlated and analyzed to detect potential optimization opportunities or anomalies like unusually high energy consumption. Due to various difficulties this data might be incomplete which makes accurate forecasting even harder.

Goal of the GECCO 2015 Industrial Challenge is to develop capable procedures to recover missing information in heating system operating data. Adequate recovery of the missing data enables more accurate forecastings which allow for intelligent control of the heating systems, and therefore contributes to a positive energy balance and reduced expenses.

Submission deadline: June 22, 2015

Official Webpage: www.spotseven.de/gecco-challenge/gecco-challenge-2015/

Includes all articles indexed by Scopus® for the seven journals listed on the Design Society website and all papers indexed for DESIGN and ICED (accessed on 05/Nov/2016).

The full search query used to extract the articles is:

"( SRCTITLE ( "Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM" OR "Journal of Engineering Design" OR "Design Studies" OR "Research in Engineering Design" OR "CoDesign" OR "Journal of Design Research" OR "Design Science Journal" OR "The International Journal of Design Creativity and Innovation" OR "International Conference on Engineering Design" OR "INTERNATIONAL DESIGN CONFERENCE" ) ) AND ( "data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*" ) AND ( EXCLUDE ( EXACTSRCTITLE , "Hardware Software Codesign Proceedings Of The International Workshop" ) OR EXCLUDE ( EXACTSRCTITLE , "Journal Of Engineering Design And Technology" ) OR EXCLUDE ( EXACTSRCTITLE , "Research In Engineering Design Theory Applications And Concurrent Engineering" ) OR EXCLUDE ( EXACTSRCTITLE , "Chinese Journal Of Engineering Design" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2005 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 12 Proceedings Of The 10th ACM International Conference On Hardware Software Codesign And System Synthesis Co Located With Esweek" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2006 Proceedings Of The 4th International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2007 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2008 Proceedings Of The 6th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2008" ) OR EXCLUDE ( EXACTSRCTITLE , "Second IEEE ACM IFIP International Conference On Hardware Software Codesign And Systems Synthesis Codes Isss 2004" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2011 Esweek 2011 Proceedings Of The 9th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 11" ) OR EXCLUDE ( EXACTSRCTITLE , "2010 IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2010" ) OR EXCLUDE ( EXACTSRCTITLE , "2013 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "2014 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2014" ) OR EXCLUDE ( EXACTSRCTITLE , "2015 ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "8th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2010" ) ) AND ( EXCLUDE ( EXACTSRCTITLE , "2015 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "9th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2011" ) OR EXCLUDE ( EXACTSRCTITLE , "11th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "10th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2012" ) OR EXCLUDE ( EXACTSRCTITLE , "A Practical Introduction To Hardware Software Codesign" ) )"

The keywords used in the article "DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA" are:

"data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*"

Korea Tourism Sales: International Conference Business data was reported at 1,837,698.000 KRW mn in 2016. This records an increase from the previous number of 1,760,254.000 KRW mn for 2015. Korea Tourism Sales: International Conference Business data is updated yearly, averaging 1,911,889.000 KRW mn from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 2,442,369.000 KRW mn in 2014 and a record low of 1,264,133.000 KRW mn in 2008. Korea Tourism Sales: International Conference Business data remains active status in CEIC and is reported by Korea National Tourism Organization. The data is categorized under Global Database’s South Korea – Table KR.H090: Tourism Industry Sales.

Each person who files bankruptcy is required to attend a meeting of creditors and respond to questions under oath from the trustee and creditors. The meetings are held nationwide. In those locations where the room is controlled by the USTP, if a participant (debtor or creditor) has limited English proficiency, an interpreter is provided free of charge via a conference phone. The number and type of languages interpreted, along with the location where the service was provided, is collected monthly by the USTP for oversight, billing, and statistical purposes. Data are provided in delimited text files. Each entry represents one interpreting session, which may include more than one case.

Each person who files bankruptcy is required to attend a meeting of creditors and respond to questions under oath from the trustee and creditors. The meetings are held nationwide. In those locations where the room is controlled by the USTP, if a participant (debtor or creditor) has limited English proficiency, an interpreter is provided free of charge via a conference phone. The number and type of languages interpreted, along with the location where the service was provided, is collected monthly by the USTP for oversight, billing, and statistical purposes. Data are provided in delimited text files. Each entry represents one interpreting session, which may include more than one case.

Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. It covers topics ranging from deep learning and computer vision to cognitive science and reinforcement learning.

This year, Kaggle is hosting the NIPS 2015 paper dataset to facilitate and showcase exploratory analytics on the NIPS data. We've extracted the paper text from the raw PDF files and are releasing that both in CSV files and as a SQLite database. Here's a quick script that gives an overview of what's included in the data.

We encourage you to explore this data and share what you find through Kaggle Scripts!

Data Description

Overview of the data in Kaggle Scripts.

nips-2015-papers-release-*.zip (downloadable from the link above) contains the below files/folders. All this data's available through Kaggle Scripts as well, and you can create a new script to immediately start exploring the data in R, Python, Julia, or SQLite.

This dataset is available in two formats: three CSV files and a single SQLite database (consisting of three tables with content identical to the CSV files).

You can see the code used to create this dataset on Github.

Papers.csv

This file contains one row for each of the 403 NIPS papers from this year's conference. It includes the following fields

• Id - unique identifier for the paper (equivalent to the one in NIPS's system)
• Title - title of the paper
• EventType - whether it's a poster, oral, or spotlight presentation
• PdfName - filename for the PDF document
• Abstract - text for the abstract (scraped from the NIPS website)
• PaperText - raw text from the PDF document (created using the tool pdftotext)

Authors.csv

This file contains id's and names for each of the authors on this year's NIPS papers.

• Id - unique identifier for the author (equivalent to the one in NIPS's system)
• Name - author's name

PaperAuthors.csv

This file links papers to their corresponding authors.

• Id - unique identifier
• PaperId - id for the paper
• AuthorId - id for the author

database.sqlite

This SQLite database contains the tables with equivalent data and formatting as the Papers.csv, Authors.csv, and PaperAuthors.csv files.

pdfs

This folder contains the raw pdf files for each of the papers.

The data sets released here has been used in our a study on longitudinal information seeking and social networking behaviors across academic communities. Social media like Twitter have been widely used in physical gatherings, such as conferences and sports events, as a "backchannel" to facilitate the conversations among participants. It has remained largely unexplored though, how event participants seek information in those situations.

There are three key results:

(1) Our study takes the first initiative to characterize the information seeking and responding networks in a concrete context---academic conferences---as one example of physical gatherings. By studying over 190 thousand tweets posted by 66 academic communities over five years, we unveil the landscape of information-seeking activities and the associated social and temporal contexts during the conferences.

(2) We leverage crowdsourcing and machine learning techniques to identify distinct types of information-seeking tweets in academic communities. We show that the information needs can be differentiated by their posted time and content, as well as how they were responded to. Interestingly, users' tendencies of posting certain types of information needs can be inferred by prior tweeting activities and network positions.

(3) Moreover, our results suggest it is also possible to predict the potential respondents to different types of information needs. Our study was based on two data sets: (1) a long-term collection of tweets posted by 66 academic communities over five years, and (2) a subset of information-seeking tweets with human annotated labels (the types of questions). We are making the data sets available for academic researchers and public use, to enable the discovery of new insights and development of better techniques to facilitate information seeking.

Dataset (1):

The conference tweets are collected through keywords search using Topsy API in 2014. The keywords vary for each conference and each year, but typically include two parts in the text and follow the format of "Conference Acronym"+"Year". For example, the International World Wide Web Conference in the year of 2013 would have the hashtag as "www2013".

Duration: 2008 to 2013 Total number of tweets: 334,507

Dataset (2):

We further identify the information seeking tweets by checking whether the tweet contains the question mark (?) in its text. We then design the information seeking question categorization and develop the code book to help human subjects identify the question type. The human annotations are obtained from Amazon Mechanical Turk. Based on the human annotations, we train machine classifiers to identify the question types for the rest of information seeking tweets.

Duration: 2008 to 2013

Total number of labeled information seeking tweets: 1,899 Total number of unlabeled information seeking tweets: 9,967

Publication:

If you make use of this data set, please cite:

Wen, X., & Lin, Y. R. (2015, November). Information Seeking and Responding Networks in Physical Gatherings: A Case Study of Academic Conferences in Twitter. In Proceedings of the 2015 ACM on Conference on Online Social Networks (pp. 197-208). ACM.

A Family Team Meeting (FTM) is a family-centered rapid response meeting CPI uses to try and prevent a removal by engaging caregivers, parents and extended family and friends to address child safety concerns. An FTM is not limited to an investigation and can occur at any point or stage in which CPI or CPS is involved with a family.

More information at www.dfps.texas.gov