Pie

## Overview

Pie is a dataset for object detection tasks - it contains Pie annotations for 579 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

PIE Dataset Card for "CoMAGC"

This is a PyTorch-IE wrapper for the CoMAGC Huggingface dataset loading script.

  Data Schema

The document type for this dataset is ComagcDocument which defines the following data fields:

pmid (str): unique sentence identifier sentence (str) cancer_type (str) cge (str): change in gene expression ccs (str): change in cell state pt (str, optional): proposition type ige (str, optional): initial gene expression level

and the following annotation… See the full description on the dataset page: https://huggingface.co/datasets/pie/comagc.

For more data on Austin demographics please visit austintexas.gov/demographics.

Classification for illumination of frontal face in CMU-PIE database.

This dataset includes County spending data for Montgomery County government. It does not include agency spending. Data considered sensitive or confidential and will be encrypted before it is posted.

PIE Dataset Card for "squad_v2"

This is a PyTorch-IE wrapper for the squad_v2 Huggingface dataset loading script.

  Data Schema

The document type for this dataset is SquadV2Document which defines the following data fields:

text (str) id (str, optional) metadata (dictionary, optional) title (str, optional)

and the following annotation layers:

questions (annotation type: Question, target: None) answers (annotation type: ExtractiveAnswer, targets: text and questions)

See… See the full description on the dataset page: https://huggingface.co/datasets/pie/squad_v2.

All states (including the District of Columbia) are required to provide data to The Centers for Medicare & Medicaid Services (CMS) on a range of indicators related to key application, eligibility, and enrollment processes within the state Medicaid and Children’s Health Insurance Programs (CHIP). These data reflect enrollment activity for all populations receiving comprehensive Medicaid and CHIP benefits in all states, as well as state program performance.

The recognition rates on CMU-PIE Face Database.

PIE Dataset Card for "DrugProt"

This is a PyTorch-IE wrapper for the DrugProt Huggingface dataset loading script.

  Data Schema

There are two versions of the dataset supported, drugprot_source and drugprot_bigbio_kb.

  DrugprotDocument for drugprot_source

defines following fields:

text (str) id (str, optional) metadata (dictionary, optional) title (str, optional) abstract (str, optional)

and the following annotation layers:

entities (annotation type: LabeledSpan… See the full description on the dataset page: https://huggingface.co/datasets/pie/drugprot.

The Pi relational database (in MySQL format) contains genetics-led drug target prioritizations for 30 immune traits, used to power a web interface http://pi.well.ox.ac.uk. For each prioritized gene, it provides priority rating, data used for predictor preparation, current therapeutic drugs with phase of development, and different measurements on druggability. Schema documentation and diagram are also provided giving instructions on how to install and use the database.

Comprehensive dataset containing 2,748 verified Pie shop businesses in United States with complete contact information, ratings, reviews, and location data.

PIE Starting Engine

## Overview

PIE  Starting Engine is a dataset for classification tasks - it contains Icons annotations for 293 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).

144 Global import shipment records of Pie Filling with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

In shotgun proteome analysis by liquid chromatography−tandem mass spectrometry (LC−MS/MS), not all coeluting peptides at a given retention time are subjected to MS/MS due to the limitation of spectral acquisition speed of a mass spectrometer. In this work, precursor ion exclusion (PIE) in an electrospray ionization (ESI) quadrupole time-of-flight (QTOF) mass spectrometer was explored as a means of mitigating the undersampling problem. This strategy is based on running replicates of the sample where the precursor ions detected in the initial run(s) are excluded for MS/MS in the subsequent run. Four PIE methods as well as running replicates without PIE were investigated and compared for their effectiveness in identifying peptides and proteins. In the analysis of an MCF-7 breast cancer cell lysate digest by three replicate 2 h gradient LC−ESI runs, the first PIE method used a list of precursor ions detected in the initial run(s) for exclusion and identified a total of 572 proteins from the three runs combined with an average of 3.59 peptides matched to a protein. The second PIE method involved in the generation of a list of m/z values of precursor ions along with their retention time information from the initial run(s), followed by entering these ions with retention times into the ion exclusion program of the QTOF control software for exclusion at a predefined retention time window (i.e., ±150 s). In comparison to the first PIE method, this method reduced the possibility of excluding different peptide ions of the same m/z (within a mass tolerance window) eluted at different retention windows. A total of 657 proteins were identified with an average of 3.75 peptides matched to a protein. The third PIE method studied relied on the exclusion of the precursor ions of peptides identified through database search of the MS/MS spectra generated in the initial run(s). This selective PIE method identified a total of 681 proteins with an average of 3.68 peptides matched to a protein. The final PIE method investigated involves the expansion of the selective PIE list by including nonidentifiable peptide ions found in the database search. This complete PIE method identified a total of 726 proteins with an average of 3.66 peptides per protein. In the case of three replicate runs without PIE, a total of 460 proteins were identified with an average of 3.51 peptides matched to a protein. Thus, the use of an optimal PIE strategy significantly increased the number of proteins identified from replicate runs (i.e., 726 vs 460 or a 58% increase). It is further demonstrated that this PIE strategy also improves protein identification efficiency in the analysis of a yeast whole cell lysate digesta less complex proteome digest. A total of 533 proteins identified from five replicate runs with complete PIE, compared to 353 proteins identified from the five replicate runs without PIE, representing a 51% increase in the number of proteins identified.

This is the digits of the number PI (3.14159...) for: 1. first one hundred thousand digits; 2. first ten million digits; 3. first one hundred million digits. This was obtained from various sources.

125 Global import shipment records of Pie Tools with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains the predicted prices of the asset Pie Token over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

PIE Dataset Card for "BioRel"

This is a PyTorch-IE wrapper for the BioRel Huggingface dataset loading script.

  Data Schema

The document type for this dataset is BioRelDocument which defines the following data fields:

text (str)

and the following annotation layers:

entities (annotation type: SpanWithIdAndName, target: text) relations (annotation type: BinaryRelation, target: entities)

SpanWithIdAndName is a custom annotation type that extends typical Span with the… See the full description on the dataset page: https://huggingface.co/datasets/pie/biorel.

Comprehensive dataset containing 62 verified Pie shop businesses in Illinois, United States with complete contact information, ratings, reviews, and location data.

This dataset is about book subjects. It has 1 row and is filtered where the books is The pie. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.