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. 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. Any data you provide to the Project is subject to the license agreement indicated in the Project’s source repository for the Materials. Signature
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. 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. 8. This Agreement begins on the final date of execution by both Parties and continues until the earlier of (i) CDPH’s decision to stop providing the COVID-19 Dataset, or (ii) two (2) years. After two (2) years, this Agreement will expire without further action. If the Parties wish to extend the Agreement, they may do so by reviewing, updating, and reauthorizing this Agreement.
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. Extremity soft-tissue sarcomas (eSTS) constitute a wide variety of histological subtypes with different sizes and grades that affect patients of any age group. Treatment protocols may differ between institutes and countries. Hence, important differences can be observed in the clinical course and prognosis of patients [27]. Over the years, several prognostic prediction models have been developed for overall survival and local recurrence [28–30]. For this project, a retrospectively collected cohort of 3826 patients with eSTS was used [29]. The dataset contained pseudo-anonymised patients from Leiden University Medical Center (Leiden, the Netherlands), Royal Orthopaedic Hospital (Birmingham and Stanmore, UK), Netherlands Cancer Institute (Amsterdam, the Netherlands), Mount Sinai Hospital (Toronto, Canada), the Norwegian Radium Hospital (Oslo, Norway), Aarhus University Hospital (Aarhus, Denmark), Skåne University Hospital (Lund, Sweden), Medical University Graz (Graz, Austria), Royal Marsden Hospital (London, UK), Xxxxxx xxx Xxxx (Rotterdam, the Netherlands), Radboud University Medical Center (Nijmegen, the Netherlands), University Medical Center Groningen (Groningen, the Netherlands), Xxxxx- land University Hospital (Bergen, Norway), Helios Klinikum Berlin-Buch (Berlin, Germany), MedUni Vienna (Vienna, Austria), Vienna General Hospital (Vienna, Austria). In addition, eSTS patients from EORTC 62931 randomised controlled trial were included [31]. Data from the centers was collected between January 2000 and December 2014. Patients from the EORTC trial were recruited between February 1995 and December 2003. Characteristics Total (N = 3826) Gender (%) Female 1713 (44.77%) Male 2113 (55.23%) Mean age in years (sd) 59.40 (18.04) Mean tumor size in cm (sd) 8.97 (5.69) Surgical margin (%) R0 3310 (86.51%) R1−2 516 (13.49%) Adjuvant chemotherapy (%) No 3350 (87.56%) Yes 476 (12.44%) Tumor grade (%) II 656 (17.15%) III 3170 (82.85%) Histological subtype (%) Myxofibrosarcoma 771 (20.15%) Synovial sarcoma 450 (11.76%) MFH/UPS/NOS 1330 (34.76%) Leiomyosarcoma 385 (10.06%) Liposarcoma 421 (11.00%) Other 469 (12.26%) Tumor depth (%) Superficial 1014 (26.50%) Deep 2812 (73.50%) Radiotherapy (%) No 1341 (35.05%) Neoadjuvant 521 (13.62%) Adjuvant 1964 (51.13%) Table 8.1: Patient demographics. sd, standard deviation; R0, negative margin; R1−2, positive margin with tumor cells in the inked surface of the resection margin; MFH/UPS/NOS, alignant fibrous histiocytoma / undif- feren...
Dataset. Figure 1: Excerpt from the AffectNet (Xxxxxxxxxxxx 2015) database. High quality emotional annotations (i.e. continuous valence-arousal dimensions) are more commonly available for facial expressions than for multi-modal data. In contrast to databases containing multi-modal emotional expressions (i.e. including high quality spoken audio), well annotated data exclusively featuring facial expressions are more broadly available. The field of image processing is by far the most advanced research topic concerning deep learning approaches. Classification problems are accessible due to their visual nature and the number of adequately annotated databases for a wide range of recognition tasks is relatively high. However, the majority of huge databases are focused on object recognition. Though there is a selection of publicly available databases, affect recognition (primarily from facial expressions) is not the most common image processing topic. Our need for high quality annotations in the valence - arousal space further limits the selection, as databases featuring only categorical emotion labels are more common. Therefore our current model is again based on transfer learning techniques and is for now mainly trained on a single comprehensive affective corpus called AffectNet (Xxxxxxxxxxxx, 2015). The collection of facial expressions consists of close-up shots of human faces taken from publicly available video and picture collections (reference). Annotations contain categorical emotion labels as well as continuous valence - arousal scores.
