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Dataset_Effects of action type and performance type on young children's performance in a causal task
Data Types:
  • Tabular Data
  • Dataset
This dataset include glutathione reactivity (RC50) and toxicity values to Tetrahymena pyriformis (IGC50) for 29 SN2 compounds activated by a carbonyl electron-withdrawing group. Calculated activation energy values (Eact) and predictions for both glutathione reactivity and toxicity to Tetrahymena pyriformis using these values are also included.
Data Types:
  • Tabular Data
  • Dataset
This dataset is about a systematic review of unsupervised learning techniques for software defect prediction (our related paper: "A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction" in Information and Software Technology [accepted in Feb, 2020] ). We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. From each paper we extracted: Title, Year, Journal/conference, 'Predatory' publisher? (Y | N), Count of results reported in paper, Count of inconsistent results reported in paper, Parameter tuning in SDP? (Yes | Default | ?) and SDP references(SDPRefs OrigResults | SDPRefs |SDPNoRefs | OnlyUnSDP). Then from within each paper, we extracted for each experimental result including: Prediction method name (e.g., DTJ48), Project name trained on (e.g., PC4), Project name tested on (e.g., PC4), Prediction type (within-project | cross-project), No. of input metrics (count | NA), Dataset family (e.g., NASA), Dateset fault rate (%), Was cross validation used? (Y | N | ?), Was error checking possible? (Y | N), Inconsistent results? (Y | N | ?), Error reason description (text), Learning type (Supervised | Unsupervised), Clustering method? (Y | N | NA), Machine learning family (e.g., Un-NN), Machine learning technique (e.g., KM), Prediction results (including TP, TN, FP, FN, etc.).
Data Types:
  • Software/Code
  • Tabular Data
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  • Document
  • Text
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Data for the paper, for the citation count regrssion
Data Types:
  • Tabular Data
  • Dataset
Texture Profile Analysis, viscosity analysis, yield stress analysis and FTIR raw data.
Data Types:
  • Tabular Data
  • Dataset
  • Document
The banking prudential indicators time series. The use of the time series is free. However, we kindly ask the user to cite one of the following papers, in which the results were first published: Pfeifer, L., & M. Hodula (2018). A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example. CNB Working Paper Series No 5/2018. Prague: Czech National Bank. or Pfeifer, L., & M. Hodula (2018). A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example. ESRB Paper Series No 82/August 2018. Frankfurt am Main: European Systemic Risk Board. Disclaimer: We provide the time series in good faight. The views expressed are those of the authors and do not necessarily reflect the official views of the CNB or ESRB.
Data Types:
  • Tabular Data
  • Dataset
Supplementary Materials for Harris et al. (2020): "Stable isotope compositions of herbivore teeth indicate climatic stability leading into the mid-Miocene Climatic Optimum, in Idaho, U.S.A." Supplementary Text + Supplementary Tables S1-S4
Data Types:
  • Tabular Data
  • Dataset
  • Document
Determining the applicability of NanoString GeoMX to the analysis of BM trephine samples. 5 samples were analysed. MDS1, Normal, MDS2, AA1 and AA2. 12 x 300um ROIs were selected in each sample and analysed using GeoMX digital spatial profiling. For MDS1, Normal and MDS2 - ROIs 1-10 are in tissue, ROI 11 in decalcified trabeculum and ROI 12 in blank slide. For AA1 and AA2 - ROIs 1-11 are in tissue and ROI 12 is in blank slide. Raw counts and technical controls (pos and neg ERCC) are shown.
Data Types:
  • Tabular Data
  • Dataset
Esse modelo foi desenhado no software treeage e foi criado um modelo tipo player onde indivíduos que não possuem o software podem fazer o download no site de uma versão do visualizador e abrir o arquivo. Para tal, é preciso acessar: www.treeage.com, clicar no menu em "Free Trial", preencher o formulário e adquirir uma licença gratuita de visualizador (Viewer license). Foi inserido também uma tabela do Excel com o cálculo dos valores mensais baseado nas posologias de tratamento e nos preços relativos a tabela da CMED de fev/2020 com PMVG de 0%. Ele permite alterar os custos de tratamento a fim de verificar o preço em que cada estrategia de tratamento se tornariam custo-efetiva. É possível realizar análise de custo-efetividade com sensibilidades determinísticas para os custos mensais e probabilística de maneira geral, simulando as distribuições inseridas.
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This is the Texas climate dataset pertaining to the following research paper: "Exploring abrupt alternations between wet and dry conditions in Texas on the basis of historical observations and convection-permitting climate model simulations" . It includes the PRISM precipitation dataset (1981-1995) downloaded at ftp://prism.oregonstate.edu/.
Data Types:
  • Tabular Data
  • Dataset
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