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  • The data is for a gravity model for East African Community Partner States from 1996 to 2018. It's for an estimation of intra-EAC trade.
    Data Types:
    • Tabular Data
    • Dataset
  • The folder contains the dataset in Fujiyama et al., Cell Reports 2018. Descriptions and technical explanations are reported in the paper.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
  • The VBA codes are embedded in the developer of .xlsm files. 'HTE_PL_ABS.xlsm' to extract and sort the raw data of PL/Abs. at different aging time. 'HTE_ABS.xlsm' to analyze the absorption data. 'HTE_PL.xlsm' to sort the PL data. 'HTE_summary.xlsm' to summarize the T80 lifetime.
    Data Types:
    • Tabular Data
    • Dataset
  • Retroscpective review of outcomes of laparoscopic Sacrocolpopexy over an extended period of time with Qol questionnaire
    Data Types:
    • Tabular Data
    • Dataset
  • Study question Is it effective for patients taking dienogest to use progestin-primed ovarian stimulation (PPOS) protocol during controlled ovarian hyperstimulation (COH), compared to PPOS with dydrogesterone (DYG)? Study answer Patients taking dienogest can continue the endometriosis treatment and get good quality embryo using PPOS during COH, despite they have severe ovarian endometriosis. What is known already Dienogest is an oral progestin effective for the treatment of symptomatic endometriosis, such as reduction of endometrial lesion and control of pain intensity with safe profile and good tolerability. Dienogest also provides complete ovulation inhibition at a daily dose of 2mg, and a rapid recovery of ovarian function after cessation of its administration. PPOS is a new COH regimen using a progestin as alternative to GnRH antagonist for blocking LH surge, and several reports have shown that DYG is an appropriate progestin for PPOS protocol. However, dienogest has not been used in PPOS protocol yet. Study design, size, duration This was a prospective controlled study of 145 women with endometriosis (aged <41) undergoing COH for IVF/ICSI and frozen embryo transfer (FET) at our infertility center from February in 2018 to November in 2019. The patients taking dienogest were allocated in Study group, and the other patients taking PPOS with DYG were allocated in Control group. Participants/ Materials, setting, methods A total of 145 patients were analyzed, PPOS with DNG: 71 patients, PPOS with DYG: 74 patients. Of the participants, 111 patients were histologically confirmed as endometriosis and 39 patients were diagnosed with published imaging criteria using transvaginal ultrasonography, respectively. Patients took DNG 2mg continuously in DNG group, and DYG were started day 3 of COH cycle. Patients were administrated with 150-225 IU of human menopausal gonadotropin (hMG) daily for COH. All viable embryos were cryopreserved for later transfer. The primary outcome measure was the clinical pregnancy rate. Main Results The number of oocytes retrieved in DNG group was less than that of DYG group (6.18±3.60 vs. 9.85±5.77, P<0.001), however, the rate of mature oocytes in DNG group was significantly higher than in DYG group [89.1% (391/439) vs. 78.9% (575/729), P<0.001].The fertilization rate was comparable between the two groups (C-IVF; 69.0% for DNG group vs. 65.1% for DYG group, P=0.510, ICSI; 80.1% for DNG protocol vs. 78.2% for DYG group, P=0.558). The clinical pregnancy rate [Odds ratio (OR) 1.15, 95%CI: 0.69~1.94, P=0.579 ] :50.5% (54/107) for DNG group vs.46.8% (59/126) for DYG group. The ongoing pregnancy rate [OR 0.70, 95%CI: 0.45~1.61, P=0.323]:55.2% (37/67) for DNG group vs.63.6% (42/66) for DYG group did not differ between the two groups.
    Data Types:
    • Slides
    • Tabular Data
    • Dataset
  • First, this dataset contains the documentation of the APS-RA (Advanced Planning Systems Reference Architecture), which is a reference architecture to assist and simplify the development of Advanced Planning Systems (APS). These are specific Decision Support System that automates the optimization of the different organizational process, aiming to provide a user-friendly interface, while automatically interacting with existing enterprise systems. This documentation is provided as a PDF, following the "Views & Beyond" documentation style. Second, APS-RA has been evaluated using ATAM (Architecture Tradeoff Analysis Method); this process is a known method for assessing software architectures. However, this process was made in two stages, with different stakeholders. This decision was made in order to deal with intrinsic characteristics of both the APS domain and of reference architectures.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • The EndNote reseach & publication dataset, which was indexed by Scopus from 1973 to 2018. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
    Data Types:
    • Tabular Data
    • Dataset
  • This database is divided into two datasets for tomato leaf images according to different image sources. The tomato leaf images of the first dataset are selected from the PlantVillage database with ten categories (nine disease categories and one health). Each image is composed of a single leaf and a single background, for a total of 14,531 images. After combining the original tomato leaf images and deleting unnecessary categories, we then adjusted the image size from 256 * 256 to 227 * 227. Afterwards, this database is divided into five subsets of 5-fold cross-validation. The detailed categories of the first dataset are: (1) Bacterial spot, (2) Early blight, (3) Healthy, (4) Late blight, (5) Leaf Mold, (6) Septoria leaf spot, (7) Target Spot, (8) Tomato mosaic virus, (9) Tomato yellow leaf curl virus, (10) Two-spotted spider mite The second dataset is images of Taiwan tomato leaves, with six categories (five disease categories and one health). It consists of a single leaf, multiple leaves, a single background and a complex background. We have 622 original images. The size of the picture is different, and we unified the image size to 227 * 227. Then we use data augmentation method to increase the number of pictures, including clockwise rotation with 90 degrees, 180 degrees, and 270 degrees; horizontal mirroring, vertical mirroring, reducing image brightness and increasing image brightness, etc. There are 4,976 images after data enhancement. The detailed categories of the second dataset are: (1) Bacterial spot, (2) Black leaf mold, (3) Gray leaf spot, (4) Healthy, (5) Late blight, (6) Powdery mildew
    Data Types:
    • Dataset
    • File Set
  • Description on the recovery rate/growth rate from the data taken from the various sources using formulations of relational between them.
    Data Types:
    • Tabular Data
    • Dataset
  • We present the raw data and the script after all the analyses.
    Data Types:
    • Software/Code
    • Image
    • Tabular Data
    • Dataset
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