Dataset

This dataset was created by Shreshth Vashisht

Released under Apache 2.0

Contents

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Dataset

This dataset was created by Nikhil Sharma

Released under CC0: Public Domain

Contents

Added new dataset OpenDataLog. The dataset stores detailed information regarding issues with the open data portal, new or changes to datasets on the portal as well as other information related to the City's Open Data Portal

These datasets each consist of a folder containing a personal geodatabase of the NHD, and shapefiles used in the HydroDEM process. These files are provided as a means to document exactly which lines were used to develop the HydroDEMs. Each folder contains a line shapefile named for the 8-digit HUC code, containing the NHD flowlines that comprise the coastline for that island. The “hydrolines.shp” shapefile contains the lines that were burned into the DEM. These lines were selected from the NHD flowlines, with some minor editing in places. The “wbpolys.shp” shapefile contains the water-body polygons that were selected from the NHD and used in the bathymetric gradient process. The folders for HUCs 20010000 (Hawaii) and 20020000 (Maui) also contain a “walls.shp” shapefile, which contains the lines that were superimposed on the surface as “walls.”

About this dataset

Context

The dataset tabulates the Kiawah Island population by age. The dataset can be utilized to understand the age distribution and demographics of Kiawah Island.

Content

The dataset constitues the following three datasets

• Kiawah Island, SC Age Group Population Dataset: A complete breakdown of Kiawah Island age demographics from 0 to 85 years, distributed across 18 age groups
• Kiawah Island, SC Age Cohorts Dataset: Children, Working Adults, and Seniors in Kiawah Island - Population and Percentage Analysis
• Kiawah Island, SC Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Here are the 143 datasets in "arff" format used in the HGS paper.

All raw data sets

Dataset

This dataset was created by Singh Prince Rinku

Released under Other (specified in description)

Contents

We explore the potential of x-ray micro computed tomography (μCT) for the field of ant taxonomy by using it to enhance the descriptions of two remarkable new species of the ant genus Terataner: T. balrog sp. n. and T. nymeria sp. n.. We provide an illustrated worker-based species identification key for all species found on Madagascar, as well as detailed taxonomic descriptions, which include diagnoses, discussions, measurements, natural history data, high-quality montage images and distribution maps for both new species. In addition to conventional morphological examination, we have used virtual reconstructions based on volumetric μCT scanning data for the species descriptions. We also include 3D PDFs, still images of virtual reconstructions, and 3D rotation videos for both holotype workers and one paratype queen. The complete μCT datasets have been made available online (Dryad, https://datadryad.org) and represent the first cybertypes in ants (and insects). We discuss the potential of μCT scanning and critically assess the usefulness of cybertypes for ant taxonomy.

TED leader-level data for 1,733 heads of government from 1945-2015

The table concept_relationship is part of the dataset MedAlign, available at https://stanford.redivis.com/datasets/48nr-frxd97exb. It contains 58831134 rows across 8 variables.

Dataset Card for "AI-Generated-vs-Real-Images-Datasets"

More Information needed

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.0", "robot_type": "unknown", "total_episodes": 1804, "total_frames": 338188, "total_tasks":24, "total_videos": 7216, "total_chunks": 2, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:1804" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/fmb.

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images ( 6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test set consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.

The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.

Basic raw materials 1:200 000 contains geoscientific data relating to basic raw material resources including clay, limesand, limestone, hard rock aggregate, sand and gravel. Show full description

Dataset

This dataset was created by JAYAPRAKASHPONDY

Released under CC0: Public Domain

Contents