This is the CC and Ex subdomains found in PH-CC-Ex globular domain from Tiam1 and Tiam2 proteins (T-lymphoma invasion and metastasis). The CC subdomain forms an antiparallel coiled coil with two long alpha-helices, together with the C-terminal Ex subdomain they form a small globular domain comprising three alpha-helices. The CC subdomain of the Tiam2 PHCCEx domain follows the C-terminal alpha1 helix of the PH [pfam:PF00169] subdomain through a four-residue linker .

Tiam1 is a guanine exchange factor (GEF) for CDC42 and the Rho-family GTPase Rac1, which plays an important role in cell-matrix adhesion and in cell migration . Tiam1 is involved in multiple steps of tumorigenesis .This entry represents the CC and Ex subdomain found in the PH-CC-Ex globular domain of the Tiam1 and Tiam2 proteins (T-lymphoma invasion and metastasis). The CC subdomain forms an antiparallel coiled coil with two long α-helices, together with the C-terminal Ex subdomain they form a small globular domain comprising three α-helices. The CC subdomain of the Tiam2 PHCCEx domain follows the C-terminal alpha1 helix of the PH subdomain through a four-residue linker .

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The main entity of this document is a structure with accession number 4k2p

Example of use of PWO: a whole publishing workflow of a journal article formally represented by means of PWO.

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Self-consistent source dataset for the Shingle project -- an approach and software library for the generation of boundary representation from arbitrary geophysical fields and initialisation for anisotropic, unstructured meshing (see https://www.shingleproject.org for more information).

Dataset

This dataset was created by Tannmay Yadav

Released under CC0: Public Domain

Contents

The Multi-domain Image Characteristic Dataset consists of thousands of images sourced from the internet. Each image falls under one of three domains - animals, birds, or furniture. There are five types under each domain. There are 200 images of each type, summing up the total dataset to 3,000 images. The master file consists of two columns; the image name and the visible characteristics of that image. Every image was manually analyzed and the characteristics for each image were generated, ensuring accuracy.

Images falling under the same domain have a similar set of characteristics. For example, pictures under the bird's domain will have a common set of characteristics such as the color of the bird, the presence of a beak, wing, eye, legs, etc. Care has been taken to ensure that each image is as unique as possible by including pictures that have different combinations of visible characteristics present. This includes pictures having variations in the capture angle, etc.

This dataset results from the molecular dynamics (MD) simulation of the photon-sample interaction. The photons are propagated through the SASE1 beamline and the SPB-SFX instrument at European XFEL, with an initial energy of 5 keV. The sample is the two-nitrogenase iron protein (2nip) with 4348 atoms. The simulation is performed with a demo version of XMDYN. The datasets were rewritten from the original XMDYN output into an hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for MD data. The dataset "pure_2nip_pmi_out.opmd.h5" conforms the openPMD metadata MD domain extension strictly, while the dataset "pure_2nip_pmi_out.opmd.ff.h5" stores form factor results additionally for SingFEL diffraction simulation.

This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.

Candidate solutions for each attribute used for the mutation process.

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This dataset results from a coherent wavefront propagation of 5 keV photons through the SASE1 beamline and the SPB-SFX instrument at European XFEL. The simulation was performed with the software WPG. The dataset was rewritten from the original WPG output into a hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for wavefront data.

This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.

This is a list of datasets published by Barcode of Life Data Systems (BOLD) that have DataCite DOIs and have also been cited in the scientific literature. Many of these citations represent the publication of the corresponding dataset, but in other cases an existing dataset has been reused.

This dataset was created by searching Google Scholar for the dataset identifier ("DS-*") followed by manual cleaning of the results, and adding citations that were missed.

The data is formatted following the requirements of the Data Citation Corpus.

Context

Typically, most publicly available datasets are created with the intent of testing classification or labeling algorithms. The primary goal of a learning algorithm that works on such datasets is to classify the data. Very few datasets exist, on which the goal of a learning algorithm is to reason out why and how the data has been classified.

Content

The Multi-domain Image Characteristic Dataset consists of thousands of images sourced from the internet. Each image falls under one of three domains - animals, birds or furniture. There are five types under each domain. There are 200 images of each type, summing up the total dataset to 3,000 images. The master file consists of two columns; the image name and the visible characteristics in that image. Every image was manually analysed and the characteristics for each image was generated, ensuring accuracy.

Images falling under the same domain have a similar set of characteristics. For example, pictures under the birds domain will have a common set of characteristics such as color of the bird, presence of a beak, wing, eye, legs, etc. Care has been taken to ensure that each image is as unique as possible by including pictures that have different combinations of visible characteristics present. This includes pictures having variations in the capture angle, etc.

The entire data is comprised of 3 primary classes, and further into 5 sub-classes for each primary class as follows: 1) Animals a) Cat; b) Dog; c) Fox; d) Hyena; e) Wolf

2) Birds a) Duck; b) Eagle; c) Hawk; d) Parrot; e) Sparrow

3) Furniture a) Bed; b) Chair; c) Sofa; d) Table; e) Nightstand

Each subclass also contains a.csvfile, with the image name, and characteristics present in the corresponding image. The exhaustive list of image characteristics are divided as follows: 1) Face: Eyes, Mouth, Snout, Ears, Whiskers, Nose, Teeth, Beak, Tongue

2) Body: Wings, Legs, Paws, Tail, Surface, Arm Rest, Base, Pillows, Cushion, Drawer, Knob, Mattress

3) Color: Brown, Black, Grey, White, Purple, Pink, Yellow, Turquoise

Abstract

Optum ZIP5 v8.0 database in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/). This dataset covers 2003-Q1 to 2020-Q2

Section 10

A Condition Era is defined as a span of time when the Person is assumed to have a given condition. Similar to Drug Eras, Condition Eras are chronological periods of Condition Occurrence. Combining individual Condition Occurrences into a single Condition Era serves two purposes:

• It allows aggregation of chronic conditions that require frequent ongoing care, instead of treating each Condition Occurrence as an independent event.
• It allows aggregation of multiple, closely timed doctor visits for the same Condition to avoid double-counting the Condition Occurrences.

%3C!-- --%3E

For example, consider a Person who visits her Primary Care Physician (PCP) and who is referred to a specialist. At a later time, the Person visits the specialist, who confirms the PCP's original diagnosis and provides the appropriate treatment to resolve the condition. These two independent doctor visits should be aggregated into one Condition Era.v

Conventions

• Condition Era records will be derived from the records in the CONDITION_OCCURRENCE table using a standardized algorithm.
• Each Condition Era corresponds to one or many Condition Occurrence records that form a continuous interval.
• Condition Eras are built with a Persistence Window of 30 days, meaning, if no occurrence of the same condition_concept_id happens within 30 days of any one occurrence, it will be considered the condition_era_end_date.

%3C!-- --%3E

The text above is taken from the OMOP CDM v5.3 Specification document.

Section 8

The DOMAIN table includes a list of OMOP-defined Domains the Concepts of the Standardized Vocabularies can belong to. A Domain defines the set of allowable Concepts for the standardized fields in the CDM tables. For example, the "Condition" Domain contains Concepts that describe a condition of a patient, and these Concepts can only be stored in the condition_concept_id field of the CONDITION_OCCURRENCE and CONDITION_ERA tables. This reference table is populated with a single record for each Domain and includes a descriptive name for the Domain.

Conventions

• There is one record for each Domain. The domains are defined by the tables and fields in the OMOP CDM that can contain Concepts describing all the various aspects of the healthcare experience of a patient.
• The domain_id field contains an alphanumerical identifier, that can also be used as the abbreviation of the Domain.
• The domain_name field contains the unabbreviated names of the Domain.
• Each Domain also has an entry in the Concept table, which is recorded in the domain_concept_id field. This is for purposes of creating a closed Information Model, where all entities in the OMOP CDM are covered by unique Concept.

%3C!-- --%3E

The text above is taken from the OMOP CDM v5.3 Specification document.

Section 12

A Drug Era is defined as a span of time when the Person is assumed to be exposed to a particular active ingredient. A Drug Era is not the same as a Drug Exposure: Exposures are individual records corresponding to the source when Drug was delivered to the Person, while successive periods of Drug Exposures are combined under certain rules to produce continuous Drug Eras.

Conventions

• Drug Eras are derived from records in the DRUG_EXPOSURE table using a standardized algorithm.
• Each Drug Era corresponds to one or many Drug Exposures that form a continuous interval and contain the same Drug Ingredient (active compound).
• The drug_concept_id field only contains Concepts that have the concept_class 'Ingredient'. The Ingredient is derived from the Drug Concepts in the DRUG_EXPOSURE table that are aggregated into the Drug Era record.
• The Drug Era Start Date is the start date of the first Drug Exposure.
• The Drug Era End Date is the end date of the last Drug Exposure. The End Date of each Drug Exposure is either taken from the field drug_exposure_end_date or, as it is typically not available, inferred using the following rules:
• The Gap Days determine how many total drug-free days are observed between all Drug Exposure events that contribute to a DRUG_ERA record. It is assumed that the drugs are "not stockpiled" by the patient, i.e. that if a new drug prescription or refill is observed (a new DRUG_EXPOSURE record is written), the remaining supply from the previous events is abandoned.
• The difference between Persistence Window and Gap Days is that the former is the maximum drug-free time allowed between two subsequent DRUG_EXPOSURE records, while the latter is the sum of actual drug-free days for the given Drug Era under the abo

Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be “drama” and “bibliography” simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.

Example

## Overview

Example is a dataset for classification tasks - it contains Plant Health annotations for 306 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset is generated the KAT data center in Paderborn University with the sampling rate of 64 KHz (Lessmeier et al. 2016). The damages were generated using both artificial and natural ways. More specifically, an electric discharge machine (EDM), a drilling, and an electric engraving were used to manually produce the artificial faults. While the natural damages were caused by using accelerated run-to-failure tests. The data collection process for both types of damages, i.e., artificial and real, was exposed under working conditions with different operating parameters such as loading torque, rotational speed and radial force. In total, the Paderborn datasets was collect under 6 different operating conditions including 3 conditions with artificial damages (denoted as domains I, J and K) and 3 conditions with real damages (denoted as domains L, M, and N). For example, the loading torque varies from 0.1 to 0.7 Nm and the radial force varies from 400 to 1000 N, while the rotational speed is fixed at 1500 RPM. Each operating condition (i.e., domain) contains three classes, namely, healthy class, inner fault (IF) class, and outer fault (OF) class. To prepare the data samples for the Paderborn dataset, we adopted sliding windows with a fixed length of 5,120 and a shifting size of 4,096 (Ragab et al. 2021). As such, we generated 12,340 for each artificial domain (i.e., I, J, and K) and 13,640 samples for each real domain (i.e., L, Mand N) respectively.

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