To manage future green spaces on campus and adopt to the changing climate, the Social Ecological Economic Development Studies (SEEDS) program of the University of British Columbia (UBC) organized a series of projects to predict the climate suitability (occurrence probability) of some common tree species existing at the UBC Vancouver campus by 2100. With a generally low climate suitability, coniferous trees are more vulnerable to the change of precipitation and temperature, and climate change is threatening the growth of coniferous trees in British Columbia, Canada. Western hemlock (Tsuga heterophylla) is a common coniferous tree species occurring at UBC Vancouver campus and was chosen to be analyzed in this study. The analysis of climate suitability was done using a MaxEnt model. As a machine-learning algorithm not requiring data about absent points, MaxEnt is considered to be a good tool for predicting the distribution of species. There were 19 bioclimatic variables tested in the analysis, and the precipitation in the coldest quarter of the year was determined to be the most important climatic variable affecting the growth of Western hemlock. The results showed that the occurrence probability of Western hemlock would drop from its current ~87% to ~10% under one of the most likely climate models in 2100. Although this result could not represent the real occurrence probability of Western hemlock by the end of the century due to the limited data and variables considered (climate only), this study provided a reference for future climate suitability analysis at UBC Vancouver campus of other tree species and could potentially help with the protection of existing Western hemlock at the campus.

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The Montréal cases were transcribed from the Register of Patients of the University Lying-in Hospital, a large leather-bound ledger now kept in the McGill University Archives, Montréal, Quebec. Because the number of patients was small, all case records were coded. The series runs from 1843-1900. Unfortunately, the information for the period 1843 to 1850 is too limited to support systematic analysis. In 1901 the hospital adopted a new form of taking case records although the data gathered remained consistent with previous practice. Unfortunately, this information was not collected as thoroughly as had been the practice before the turn of the century. The series ends abruptly and inexplicably in 1905. The initial data base included 8216 cases.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Significant progress has been made globally in reducing GHG emissions from the operation of buildings, however, huge challenges still remain in mitigating the embodied emissions from the manufacturing, transportation, and disposing of building materials. This is particularly relevant in BC, where electricity is largely generated from renewable sources, indicating limited potential for further reducing GHG emissions from building operations. Therefore, investigating the options to reduce embodied GHG emissions in building materials presents another crucial opportunity to further mitigate the overall GHG emissions from buildings. For example, the City of Vancouver’s newly released Climate Emergency Action Plan has set an ambitious goal of reducing 40% embodied GHG emissions in new buildings compared to the 2018 benchmark.

To support decision-making that could ultimately fulfill such an ambitious goal, it is imperative that a standard approach is used to derive benchmark buildings and the corresponding bill-of-materials (BoM). Accordingly, we compiled a BoM dataset of 35 typical buildings in Canada. The data was classified into “whole-building level” and “assembly-level”, and building materials were sorted by an aggregation system (see below) in both classifications. Whole-building-level BoM contains data for 33 buildings, including institutional buildings and residential houses at the University of British Columbia, container-based single-family housing, single-family residential building, precast concrete commercial buildings, etc. On the other hand, assembly-level BoM contains material data for different structural components of one multi-unit apartment and one typical newly-built single-family home in Vancouver.

The aggregation system organizes the material data by three tiers - M1, M2, and M3, which offers 3 hierarchical levels of specificity. The first hierarchical level (M1) provides the least specified information while the final level (M3) provides the most detailed information. For example, Aluminum cold-rolled sheet (M3) is categorized within Aluminum (M2) under Metal (M1). This aggregation system offers the flexibility for LCA practitioners to obtain BoM information at the resolution that fits their scope of work.

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The clinical records of the Boston Lying-in inpatient and outpatient services, and those of the New England Hospital maternity unit, are housed in the Rare Book Room, Francis A. Countway Library of Medicine, Harvard University, Boston, Massachusetts. While the information found in these records varied somewhat from one hospital to the next, each set of records was consistent throughout the period under review. Four data bases were established, one consisting exclusively of white patients for each of the three clinics and one composed of all black patients from both services of the Boston Lying-in. The four sample populations were constituted in the following ways. The clinical records of the New England Hospital’s maternity clinic exist in continuous series from 1872 to 1900. All births were recorded because there were fewer than 200 deliveries annually. The patient registers of the Boston Lying-in inpatient service span the years 1886-1900, with a gap in 1893 and 1894. A random sample of 200 cases was chosen for each year. The same procedure was followed at the outpatient clinic, whose case files extend from 1884 to 1900, excepting those years in which all were recorded because fewer births occurred, and a short period when all cases were noted even though they totaled more than 200. Because the number of black patients was small, and because the birth weight experience of blacks was distinctive in some important respects, a fourth file was created consisting of all blacks in the Lying-in inpatient and outpatient records. The preliminary data bases consisted of 3480, 2503, 3654, and 373 cases, respectively. The birth weight means in the Lying-in inpatient sample are accurate to 79 grams, and those of the outpatient clinic sample to 65 grams, at the 95 percent confidence level.

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The Dublin patients are a random sample of those found in the clinical records of the hospital. Case files were compiled from two sources, the Register of Patients, which included the administrative record of each patient, and the Master’s Ward Book, which noted the medical circumstances of each case. These records exist in continuous series during the years with which this study is concerned, and only minor changes occurred in the categories of information collected. Most of these documents were held by the Rotunda Hospital when they were consulted for this project, but all of them have now been transferred to the Public Record Office of Ireland in Dublin. As birth weights were first recorded in July 1869, 100 cases were selected for that year. In all subsequent years 200 cases were chosen. The preliminary data base consisted of 12,454 cases. The weight and length means in the sample are accurate to 84 grams and 0.4 centimeter at a confidence level of 95 percent

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The data analyzed in this study were taken from two sources, the Register of Birth and the Indoor Casebooks of the Edinburgh Royal Maternity Hospital. The bulk of the records are kept in the Medical Archives Centre, University of Edinburgh, in Edinburgh, although the Library of the Royal College of Physicians in Edinburgh holds the Indoor Casebook for 1844-71. In 1877 the hospital adopted a new form of taking case records, introducing discontinuity into the time series created for this study. The most significant change was a loss of some information about the social and economic backgrounds of the hospital patients. In years when fewer than 200 patients were admitted to hospital all cases were recorded. In other years a random sample of 200 patients were selected for analysis. Patient records were missing for 1852-53 and 1858-63. In all, 13,488 cases were chosen for preliminary analysis. The newborn weights and lengths in the sample are accurate to 91 grams and 0.6 centimeter at the 95 percent confidence level.