Filter Results
280475 results
These datasets involve 1) ambient air quality testing, 2) spontaneous combustion fire frequency record, 3) temperature anomalies detected by Landsat, 4) photos of mine waste heap as well as affected environment, 5) estimation of remedial cost, 6) VDO of gas emission from a crack on top of the mine waste heap, and 7) XRD analysis of coal-mine waste
Data Types:
  • Other
  • Image
  • Video
  • Tabular Data
  • Dataset
  • Document
Total RNA was purified from E. pacifica using with an RNeasy Lipid tissue mini kit. The library of E. pacifica for next generation sequencing was made using with a TruSeq RNA library prep kit v2 (Illumina). RNA purification and library preparation were performed according to the manufacturers’ instructions. The library was analyzed by Miseq using a Miseq reagent kit v3 (600 cycle) (Illumina). The fastaq data was assembled by Trinity.
Data Types:
  • Dataset
  • Text
The repository contains the ERP data for self-face, friend's face and other's face perception. Raw Data folder contain the EEG data in Brain Products format. Epoched Data folder contain processed EEG data in EEGLAB format. sLORETA files folder contain data of source mean amplitude within-cluster of significant correlations between ERP and heartbeat perception scores. Also, repository include subject description file with the antropometric and psychometric data.
Data Types:
  • Other
  • Software/Code
  • Tabular Data
  • Dataset
  • Text
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
  • Dataset
  • Document
  • Text
  • File Set
Smoke Test 17Feb2020 rdmmibtest1 (Dataset-1)
Data Types:
  • Dataset
  • Text
Dual numbers are used to develop methods for computing velocities and accelerations
Data Types:
  • Software/Code
  • Dataset
  • Text
Tank Detection and Count Dataset contains 760 satellite image tiles of size 512*512 pixels and one-pixel cover 30cm*30cm at ground level. Each tile is associated with .xml and .txt files. Both .xml and .txt file contains the same annotations of oil/gas tanks but in a different format. .xml contains the pascal VOC format and in .txt file, every line contains the class of the tank and four coordinates of the bounding box: xmin, ymin, xmax, ymax.
Data Types:
  • Image
  • Dataset
  • Document
  • Text
Smoke Test on 17Jul2019 natscilivecustomer (Dataset-1) Smoke Test on 17Jul2019 natscilivecustomer (Dataset-2)
Data Types:
  • Other
  • Software/Code
  • Image
  • Video
  • Tabular Data
  • Dataset
  • Document
  • Text
  • Audio
Associated research in : Gordon, B. L., Paige, G. B., Miller, S. N., Claes, N., & Parsekian, A. D. (2020). Field scale quantification indicates potential for variability in return flows from flood irrigation in the high altitude western US. Agricultural Water Management, 232, 106062. Readme: The included files are: Calculated Flow, Calculated_Losses, Calculated_Return_Flows, ET_Not_Interpolated, Precipitation, and GIS Database. All the data (except GIS) are in tab delimited ASCII files. GIS data are in standard formats, most site specific information including soils, meadow delineation, instrumentation, etc. can be found in the site_information file. Flow data (Calculated_Flow, Calculated_Losses, Calculated_Return_Flows) were obtained using developed rating curves at each site, where each stilling well was instrumented with a pressure transducer (Level TROLL 500 Data Logger, In-Situ, USA) and manual flow measurements consisting of 25+ individual points for each measurement were made using an electromagnetic current meter (MF Pro, OTT Hydromet, USA). ET data include both measurements from a Large Aperture Scintillometer (LAS MKII, Kipp & Zonen, NLD) and from Penman-Monteith Calculations performed on raw meteorological data collected on site. For Penman-Monteith, we include both raw values and values modified using a crop coefficient from Pochop et al. (1992). Precipitation data were collected using a tipping bucket rain gauge (Rain Collector II, Davis Instruments, USA). All data (except the ET data for the scintillometer) are from May 2015 to October 2015; the ET data from the scintillometer are from June 2015 to October 2015. If you have any questions, or would like raw flow data or unprocessed meterological data, please contact me via email at: beatrice.gordon1@gmail.com
Data Types:
  • Dataset
  • Text
  • File Set
Batch mesophilic 37oC reactors fed with acetic acid (0.5 mL AC/L every 5-6 days), have been amended with different amount of incineration bottom ash/ and ammonium chloride for 120 days. At the start of the experiments, different mass of IBA was added to the reactors which had been amended with IBA. Then the group of the reactors which amended with NH4CL had received 4 g/L NH4Cl every run of 5-6 days. In parallel batch reactors without IBA/ and NH4Cl were also run. Reactor performance (methane production) and stability (pH drop and VFA accumulation) were investigated. On day end of the experiments i.e. on day 120, a representative digestate sample was collected from each reactor, then sequenced for 16S rRNA gene. The sequence files shown in this data set are fastq files from the illumina sequencing analysis.
Data Types:
  • Software/Code
  • Dataset
  • Text
  • File Set