Dataset. 4.1. The Dataset will consist of all the files transferred by the Depositor and the metadata provided by the Depositor. Metadata is understood to mean the contents of all fields that must be completed in the archival system at the time of deposit in order to describe the Dataset.
Dataset. Information or data from a database Service primarily devoted to market sizing by market segment and country, derived from a single (a) market segment and (b) country.
Dataset. The database we used has been developed within the subalpine GIG. Samples were collected between 1997 and 2010 in the sublittoral zone of 19 Austrian, 25 German, 21 France and 28 Italian subalpine lakes. 10 of those lakes were sampled in 2 different years while 1 lake was sampled in 3 different years, each lake-year combination has been considered as an indipendent sample unit. Invertebrates were indentifyed to the lower taxonomic level possible, mostly to genus/species level. Data gathered in more than one sampling site were aggregated to lake-year level. Environmental variables We considered climatic and morphological environmental variables (table 1): precipitation, mean annual temperature, difference between temperature in July and in January, lake surface area, lake mean depth and catchment area. The climatic data were gathered from the Climatic Research Unit (CRU) model (New et al. 2002; xxxx://xxx.xxx.xxx.xx.xx/). Table 1: Environmental variables ranges. min max Mean annual Prec. (cm) 60.16 162.67 Mean annual Temp. (°C) 5.17 12.99 T(July)-T(January) (°C) 17.40 21.60 surface (km2) 0.04 79.90 mean depth (m) 3.20 53.21 catchment (km2) 1.01 4551.60
Dataset. Dataset description The data basis was compiled within the lake macroinvertebrate groups of the AL- and CB-GIG. 7 countries with existing assessment systems for eulittoral macroinvertebrates and 4 additional countries contributed data. 9 countries are represented in the CB-GIG (table 1) and 3 in the AL- GIG (table 2).
Dataset. The Dataset will consist of all the files transferred by the Depositor and the metadata provided by the Depositor as described in Section 1. Metadata is understood to mean the contents of all fields that must be completed in the archival system at the time of deposit in order to describe the Dataset. The Depositor warrants that the Dataset corresponds to the metadata provided by the Depositor in the Data Deposit Form. The Depositor will provide the files in a preferred format, as defined on ISSDA’s File Format Policy at the time of deposit. In the event that a format is not defined as a preferred format, the Depositor will contact ISSDA before delivery. A different file format may only be supplied with the written consent of ISSDA. The Depositor will provide documentation with the Dataset that explains its creation, contents and any specific values (such as codes, characters and abbreviations), its structure (such as folder structures and relationships between files) and its actual use (such as that of software) to third parties (“Related Documentation”). The Depositor acknowledges that the Related Documentation described in Section 1 and shared by the Depositor shall be available to Researchers via ISSDA’s website without restriction. ISSDA will make the metadata associated with the Dataset freely available. The metadata associated with the Dataset will be included in ISSDA’s databases and publications and will be accessible to everyone. The Depositor will make the Dataset available to ISSDA in a manner and through a medium that ISSDA deems suitable. GDPR The Depositor has identified the Dataset as containing personal data. The Depositor shall pseudonymise any personal data included in the Dataset which shall be made available to ISSDA under a unique corresponding code and without any directly identifiable personal data. The metadata and file names shall not contain any personal data. Only bibliographical data which exclusively refer to personal data that are necessary for the accountability of the Dataset, such as its creator, rights holders and citations (hereinafter: “Bibliographical Data”) are allowed. It is explicitly forbidden to include directly identifying personal data in the deposited Dataset, the metadata and file names. The Depositor represents and warrants that any personal data (as defined by Article 4 of the GDPR) contained in the Dataset has been processed in accordance with all applicable legislation and regulations relating to the...
Dataset. Dataset(s) will be submitted solely in connection with the Research Project as defined below and described in the PI/Submitter Information and Certifications section. This SA covers only the Research Project described in the PI/Submitter Information and Certifications section. The PI/Submitter will submit a completed SA for each research project for which submission is requested. The dataset that the PI/Submitter uploads may be shared on mapMECFS with anyone who has agreed to the terms in the mapMECFS DUA for downloading the dataset, provided that the PI/Submitter retains the ownership of the data. The PI/Submitter acknowledges that RTI International employees or contractors will be able to see and use the data for the purposes of administering the site and the data repository. It is the PI/Submitter’s responsibility to ensure that (1) no personally identifiable information (PII) is present, (2) study participant privacy is fully protected, and (3) sharing is compliant with all other governing policies (e.g., Institutional Review Board [IRB]-approved protocols, informed consent, embargos). If data sharing regulations change or data have been shared inappropriately, it is the PI/Submitter’s responsibility to remove the data or request help from mapMECFS site administrators by emailing xxxxxxxx@xxx.xxx.
Dataset. The dataset is comprised of information from death certificates collected by the Georgia Department of Public Health. The death certificates come from all hospitals across the state of Georgia. The date range is 01/01/2011-12/31/2016. Variables There were five variables analyzed in this study. The variable age describes the age at which a person died. In this study, age is categorized into groups with the first group consisting of individuals who were less than 15 years old at the time of death. The groups are then divided into ten-year increments. The last group includes all individuals who were 75 years old and older at the time of their death. Gender is a dichotomous variable that describes the sex listed on the death certificate. Males were coded as 1 and females were coded as 0. Race is a categorical variable that describes the race listed on an individual’s death certificate. Death is a dichotomous variable that describes the manner in which a person died. This study codes death=1 as fatal opioid-related overdose and death=0 as a fatal motor vehicle accident. Level of education is a categorical variable that describes the highest level of education attained at the time of an individual’s death. This analysis categorizes level of education into three categories: no high school diploma or GED, high school diploma or GED, and some college or more. Inclusion criteria for cases
Dataset. For the evaluation of the authentication components a state-of-the-art dataset was used. This dataset was selected for a number of reasons including that it provided the required number of users (10 users), the duration of the experiment that lasted 10 weeks and also the fact that the dataset included all the required data for the BehavAuth system. Among the students who completed the study, are undergraduates and graduate students. In terms of gender, the dataset included both males and females. The participants that were involved in the study were enrolled in the CS65 Smartphone Programming class, a computer science programming class at Dartmouth College that is offered to both undergraduate and graduate students during Spring term in 2013. All students were offered an unlocked Android Nexus 4s to complete assignments and class projects. Each mobile phone had deployed a data collection app that was monitoring the selected sensors and without any interaction with the user, the mobile phones were uploading the data to the backend server. The data off-loading was performed only when the device was charging and only through the WiFi interface. The uploaded data were monitored through scripts to understand if the users are using the phones and participating in data collection.
Dataset. Initially there was a need to extract only the sensor data related to the BehavAuth system. This extraction refers to the Bluetooth and Location measurements for the first 10 users.
Dataset. We test our network marker selection framework in the Emory-Georgia Tech Predic- tive Health Initiative Cohort of the Center for Health Discovery and Well Being. This is an ongoing, cohort of generally healthy university employees, ages 18 and older, re- cruited between January 2008 and February 2013 (http: //xxxxxxxxxxxxxxxx.xxxxx.xxx) (Xxxxxxx, 2010). All participants are free of any acute illness, uncontrolled or unsta- ble chronic disease, hospitalizations within the year prior to study entry, substance or drug abuse within the past year, or active malignant neoplasm or history of malig- xxxxx other than basal cell skin cancer within the previous 5 years. Subjects undergo an extensive medical and metabolic assessment annually. The study is approved by the Emory Institutional Review Board, and all participants provide informed con- sent prior to any testing. For this study, only subjects with available high-resolution plasma metabolomics data are assessed (N = 371). For metabolic network, we use the KEGG human metabolic network (Kanehisa et al., 2016), and removed all nodes with degrees of 20 or higher. Such highly connected nodes are involved in too many reactions for their concentration level to be informative. In addition, the subnetwork surrounding such a node may be too diverse to carry a clear biological theme. From a network analysis point of view, the presence of such nodes makes the distance between most node pairs very small, making it difficult to select meaningful subnetworks. We conducted a systematic study of network characteristics versus the cutoff value, and determine 20 is a good cutoff value.
