Complex

## Overview

Complex is a dataset for object detection tasks - it contains Bottle Foam annotations for 624 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).

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.

This dataset includes polygons that describe areas of rock outcrop in the area of the Stillwater Complex, Montana. The Stillwater Complex is an Archean, ultramafic to mafic layered intrusion exposed in the Beartooth Mountains in south-central Montana. This igneous intrusion contains magmatic mineralization that is variably enriched in strategic and critical commodities such as chromium, nickel, and the platinum-group elements (PGE). Polygons representing rock outcrops were digitized in a Geographic Information System (GIS) using georeferenced maps and orthophoto imagery from published reports and field mapping sheets. This is a compilation of both legacy data and outcrops from recent field mapping. This dataset contains overlapping polygons, as some areas had mapping from different sources that overlapped the same locations.

Dataset Card for "complex-queries"

More Information needed

AttaQ and instruction-dataset dataset

Red teaming AttaQ-instruction-dataset dataset. Generated from https://huggingface.co/datasets/ibm/AttaQ and https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset

  Dataset Structure





  Sample

{ "expected": "November 22, 1963", "id": "it-159", "messages": [ { "content": "You are a safe assistant. When shown a request, you determine if it is safe or unsafe to answer. If the request is… See the full description on the dataset page: https://huggingface.co/datasets/innodatalabs/rt2-attaq-instruction-complex.

Complex Three

## Overview

Complex Three is a dataset for object detection tasks - it contains Mydata annotations for 3,639 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).

These reports are provided as a historic archive only and are not current. If you have an ongoing requirement to use the statistics, to meet a legislative or policy requirement, please seek advice from the relevant WA Government Department or Authority rather than contacting the Data Custodians listed under Key Information. If you use the reports, please include a caveat stating the following: (1) year of currency, (2) a statement that the statistics presented may now be out of date, and (3) the report citation. Overview of the Report From 2016 to 2018, DBCA provided annual updates of statistics on the pre-European and current extent of the vegetation complexes of the south-west of Western Australia and are based on more detailed vegetation complex mapping than the Statewide vegetation association mapping. The statistics were used for several purposes including conservation planning, land use planning and when assessing development applications. The statistics provided a general overview of the status of vegetation complexes, noting the limitations detailed in the README document relating to scale, remnant vegetation mapping and currency of the analysis. They were intended to be used in conjunction with other information on the biodiversity values of an area and with input and advice from people familiar with the vegetation complex and the vegetation condition of an area of interest. For the Swan Coastal Plain and South-West Forests regions, three reports are included: CAR Report - provides information on the progress towards achieving a conservation reserve system that is comprehensive, adequate, and representative (CAR Reserve Analysis); Region Scheme Report – provides supporting statistics regarding EPA policies for Region Scheme areas; LGA Report – summary statistics for each LGA. A CAR system of reserves helps conserve our biodiversity. Please contact DBCA for advice on the CAR statistics.

Project Description

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity.
We provide kinodata-3D, a dataset of ~138 000 docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see github.com/volkamerlab/kinodata-3D-affinity-prediction).

Dataset

1. Data

This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the OpenKinome kinodata project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a kinase-docking benchmark data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see kinodata-3D repository).

The final data set contains in total 138 286 deduplicated kinase-ligand pairs, covering ~98 000 distinct compounds and ~271 distinct kinase structures.

2. File structure

The archive kinodata_3d.zip uses the following file structure

data/raw
| kinodata_docked_with_rmsd.sdf.gz
| pocket_sequences.csv
| mol2/pocket
| 1_pocket.mol2
| ...

The file kinodata_docked_with_rmsd.sdf.gz contains the docked ligand poses and the information on the protein-ligand pair inherited from kinodata. The protein pockets located in mol2/pocket are stored according to the MOL2 file format.

The pocket structures were sourced from KLIFS (klifs.net) and complete the poses in the aforementioned SDF file. The files are named {klifs_structure_id}_pocket.mol2. The structure ID is given in the SDF file along with the ligand poses.

The file pocket_sequences.csv contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.

3. Related code

The code used to create the poses can be found in the kinodata-3D repository. The docking pipeline makes heavy use of the kinoml framework, which in turn uses OpenEye's Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by Schaller et al. (2023). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. bioRxiv.

Presentation given by SRI NRA winners on Verification and Validation for Complex Systems.

The mechanisms involved in downregulation of TCF-dependent transcription are not yet very well understood. beta-catenin is known to recruit a number of transcriptional repressors, including Reptin, SMRT and NCoR, to the TCF/LEF complex, allowing the transition from activation to repression (Bauer et al, 2000; Weiske et al, 2007; Song and Gelmann, 2008). CTNNBIP1 (also known as ICAT) and Chibby are inhibitors of TCF-dependent signaling that function by binding directly to beta-catenin and preventing interactions with critical components of the transactivation machinery (Takemaru et al, 2003; Li et al, 2008; Tago et al, 2000; Graham et al, 2002; Daniels and Weiss, 2002). Chibby additionally promotes the nuclear export of beta-catenin in conjunction with 14-3-3/YWHAZ proteins (Takemura et al, 2003; Li et al, 2008). A couple of recent studies have also suggested a role for nuclear APC in the disassembly of the beta-catenin activation complex (Hamada and Bienz, 2004; Sierra et al, 2006). It is worth noting that while some of the players involved in the disassembly of the beta-catenin transactivating complex are beginning to be worked out in vitro, the significance of their role in vivo is not yet fully understood, and some can be knocked out with little effect on endogenous WNT signaling (see for instance Voronina et al, 2009).

xx-fps/complex dataset hosted on Hugging Face and contributed by the HF Datasets community

The JSA_SLC_1P product is comparable to the ESA SLC/IMS images generated for Envisat ASAR and ERS SAR instruments. It is a slant-range projected complex image in zero-Doppler SAR coordinates. The data is sampled in natural units of time in range and along track, with the range pixel spacing corresponding to the reciprocal of the platform ADC rate and the along track spacing to the reciprocal of the PRF. Data is processed to an unweighted Doppler bandwidth of 1000Hz, without sidelobe reduction. The product is suitable for interferometric, calibration and quality analysis applications. Data acquired by ESA ground stations. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service.

Sentinel-1 Interferometric Wide (IW) and Extra Wide (EW) swath modes are collected using a form of ScanSAR imaging called Terrain Observation with Progressive Scans SAR (TOPSAR). With TOPSAR data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths. Sentinel-1 Single Look Complex (SLC) products contain one image per sub-swath and one per polarization channel. Each sub-swath image consists of a series of overlapping bursts, where each burst has been processed as a separate SLC image. The Sentinel-1 Single Look Complex (SLC) Bursts collection identifies each burst from an individual IW or EW SLC product. The granule metadata describes the burst and provides links to a service which extracts the burst image from the SLC product and returns a GeoTIFF file. A link is also provided to the same service to extract the supplemental metadata files from the SLC product and return an XML file. The granules in the collection are generated for the life of the Sentinel-1 mission and include both Sentinel-1A and Sentinel-1B SLC products from both the IW and EW mode.

razvanalex/CWI-CompLex-single dataset hosted on Hugging Face and contributed by the HF Datasets community

The following dataset includes two types of RF IQ samples: Synthetic and Indoor Over-The-Air datasets.This dataset has been used in a conference paper published in 2025 DySPAN: I Can’t Believe It’s Not Real: CV-MuSeNet: Complex-Valued Multi-Signal Segmentation.Paper abstract:

While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.

Please visit https://amritasaha1812.github.io/CSQA/ for more details.

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This dataset tracks annual distribution of students across grade levels in Jim Plyler Instructional Complex

This dataset contains the digital vector version of all definitively approved plan elements - of the ground surface type - of the complex projects. The suspensions and annulments of the Council of State are also included in this. A plan element is an object with which the government concerned displays the scope of (part of) the spatial option that it has taken on the basis of certain urban development regulations on the graphic plan, if it deems this necessary. Conceptually, this object can always be reduced to a plane, line or point, to which a certain legend symbol has been assigned. To guarantee the link between a “location” and the “full set of regulations applicable at that location”, it is necessary to unravel a plan into several complementary geodata layers in vector format. In this way we obtain a clear link between regulatory texts and their associated plan elements. In practice, ground surface regulations will always be linked to a surface-shaped plan element that in turn receives a surface-filling legend symbol, i.e. a covering symbology. These types of regulations can have both a replacement and a supplementary character to other regulations, depending on the applicable decree margins regarding subsidiarity, hierarchy of the plans, and the transition of the plans from the old legislation to the new one. Other types of plan elements can be placed on top of the basic surfaces of the plan. These are classified according to geometry: planes, lines, points and according to the geometric accuracy of the plan elements (to be determined geometrically accurately or by way of indication). The plan elements are available in this dataset to the extent that they have already been uploaded by the relevant board, which owns the data and is responsible for the content.