To obtain the prices of parts from the manufacturing characteristics and other manufacturing processes, feature quantity expression is innovatively applied. By identifying manufacturing features and calculating the feature quantities, the feature quantities are described in the form of assignments as data. To obtain the prices of parts intelligently, the most widely used and mature deep-learning method is adopted to realize the accurate quotation of parts

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This dataset used in the experiment of paper "Bus Ridesharing Scheduling Problem". This is a real-world bus ridesharing scheduling problem of Chengdu city in China, which includes 10 depots, 2,000 trips.

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This is the dataset used in the experiment of paper "Bus Pooling: A Large-Scale Bus Ridesharing Service". The dataset contains 60,822,634 trajectory data from 11,922 Shanghai taxis from one day (Apr 1, 2018). The 100 groups of coordinate sets containing three coordinates as experimental samples are used to compare the effectiveness and efficiency of location-allocation algorithms.

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This dataset refers to the case study performed in the paper "A Real Options Market-Based Approach to Increase Penetration of Renewables", submitted to IEEE Transactions on Smart Grid. The file contains the Midcontinent ISO data used for the day-ahead prices, as well as the wind data from NREL's Wind Integration National Dataset Toolkit which was used to estimate the renewable productions in the case study.

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A new dataset named Sanitation is released to evaluate the HAR algorithm’s performance and benefit the researchers in this field, which collects seven types of daily work activity data from sanitation workers.We provide two .csv files, one is the raw dataset “sanitation.csv”, the other is the pre-processed features dataset which is suitable for machine learning based human activity recognition methods.

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An emulator for the Viessmann Vitorond 200 Gas Fired Boiler VD2 Series 380 dataset was created in Matlab/Simulink based on the Simscape boiler model.

Instructions: 

Either Condition or Class should be used as data labels and the unused label should be excluded for analysis.

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The pressure sensors are represented by black circles, which are located in the three zones of each foot. For the left foot: S1 and S2 cover the forefoot area. S3, S4, and S5 the midfoot area. S6 and S7 the rearfoot or heel area. Similarly, for the right foot: S8 and S9 represent the forefoot area. S10, S11, S12 the midfoot area. S13 and S14 the heel area. The values of each sensor are read by the analog inputs of an Arduino mega 2560.

Instructions: 

Each label correspond to:

Label         Position                                             Pressure on sensors

1                normal footstep                                Left foot: S1, S2, S3, S4, S6, S7

                                                                           Right foot: S8, S9, S11, S12, S13, S14

 

 2                flat footstep                                     Left foot: S1, S2, S3, S4, S5, S6, S7

                                                                           Right foot: S8, S9, S10, S11, S12, S13, S14

 

 3                cavus footstep                                 Left foot: S1, S2, S3, S6, S7

                                                                           Right foot: S8, S9, S12, S13, S14

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A two-year electricity consumption data of a hotel building in Shanghai, China and and corresponding outdoor weather data.

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The present dataset is based on implementing of 3 approaches  with respect to the acquisition of driver data. The same one that we propose to use a sensor of concentration of alcohol in the environment (physiological), a sensor that measure the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).

 

Instructions: 

The present dataset is based on implementing 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of the concentration of alcohol in the environment (physiological), a sensor that measures the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).

 

 Number of Instances: 390 (217 for no alcohol presence

                                          173 for alcohol presence with 

                                                   different concentration)

 

 Number of Attributes: 5 numeric, predictive attributes and the class

 

 Attribute Information:

   1. acohol concentration in the car environment in ml/L

   2. car environment temperature in degrees Celsius

   3. face temperature min in degrees Celsius

   4. face temperature max in degrees Celsius

   5. pupil ratio

   6. class: 

       1 No acohol presence

       2 acohol presence

 

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This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

Instructions: 

SEER Breast Cancer Dataset

Abstract: This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

 

AGE

SEER*Stat Name: Age at diagnosis Field Description: This data item represents the age of the patient at diagnosis for this cancer. The code represents the patient’s actual age in years.

 

RACE

SEER*Stat Name: Race recode (White, Black, Other) Field Description: Race recode is based on the race variables and the American Indian/Native American IHS link variable. This recode should be used to link to the populations for white, black and other. It is independent of Hispanic ethnicity. For more information, see : http://seer.cancer.gov/seerstat/variables/seer/race_ethnicity.

1

White

2

Black

3

Other (American Indian/AK Native, Asian/Pacific Islander)

4

Other unspecified (1991+)

5

Unknown

 

 

MARITAL STATUS

SEER*Stat Name: Marital status at diagnosis

Field Description: This data item identifies the patient’s marital status at the time of diagnosis for the reportable tumor.

1

Single (never married)

2

Married (including common law)

3

Separated

4

Divorced

5

Widowed

 

T STAGE

SEER*Stat Name: Breast Adjusted AJCC 6th T (1988+) Field Description: Created from merged EOD 3rd Edition and Collaborative Stage disease information. Currently only available for Breast schema. For more information see http://seer.cancer.gov/seerstat/variables/seer/ajcc-stage/6th.

1

T1

2

T2

3

T3

4

T4

 

N STAGE

SEER*Stat Name: Breast Adjusted AJCC 6th N (1988+)

Field Description: Created from merged EOD 3rd Edition and Collaborative Stage disease information. Currently only available for Breast schema. For more information see http://seer.cancer.gov/seerstat/variables/seer/ajcc-stage/6th.

1

N1

2

N2

3

N3

 

6TH STAGE

SEER*Stat Name: Breast Adjusted AJCC 6th Stage (1988+)

Field Description: Created from merged EOD 3rd Edition and Collaborative Stage disease information. Currently only available for Breast schema. For more information see http://seer.cancer.gov/seerstat/variables/seer/ajcc-stage/6th.

1

IIA

2

IIB

3

IIIA

4

IIIB

5

IIIC

 

GRADE SEER*Stat Name: Grade Field Description: Grading and differentiation codes of 1-4, 9 are defined in ICD-O-2; 1992. Grade information may be incomplete for cases diagnosed before 1977. In the early 1980’s, additional codes specifying T-cell, B-cell, or null cell involvement in lymphomas and leukemias (histologies M9590-9940) were introduced by SEER. Because the reporting requirements and medical terminology have changed over time, care should be exercised when analyzing this information.

1

Grade I; grade i; grade 1; well differentiated; differentiated, NOS

2

Grade II; grade ii; grade 2; moderately differentiated; moderately differentiated; intermediate differentiation

3

Grade III; grade iii; grade 3; poorly differentiated; differentiated

4

Grade IV; grade iv; grade 4; undifferentiated; anaplastic

 

A STAGE

SEER*Stat Name: SEER historic stage A

Field Description: Derived from Collaborative Stage (CS) for 2004+ and Extent of Disease (EOD) from 1973-2003. It is a simplified version of stage: in situ, localized, regional, distant, & unknown. Over time several different EOD schemes have been used. Thus caution should be used when doing trend analysis. For more information including sites and years for which it isn't calculated, see http://seer.cancer.gov/seerstat/variables/seer/lrd-stage.

1

Regional — A neoplasm that has extended 1) beyond the limits of the organ of origin directly into surrounding organs or tissues; 2) into regional lymph nodes by way of the lymphatic system; or 3) by a combination of extension and regional lymph nodes.

2

Distant — A neoplasm that has spread to parts of the body remote from the primary tumor either by direct extension or by discontinuous metastasis (e.g., implantation or seeding) to distant organs, issues, or via the lymphatic system to distant lymph nodes.

 

TUMOR SIZE

SEER*Stat Name: CS tumor size (2004+)

Field Description: Information on tumor size. Available for 2004+. Earlier cases may be converted and new codes added which weren't available for use prior to the current version of CS. Each indicates exact size in millimeters. For more information, see http://seer.cancer.gov/seerstat/variables/seer/ajcc-stage.

 

ESTROGEN STATUS

SEER*Stat Name: ER Status Recode Breast Cancer (1990+)

Field Description: Created by combining information from Tumor marker 1 (1990-2003) (NAACCR Item #=1150), with information from CS site-specific factor 1 (2004+) (NAACCR Item #=2880). This field is blank for non-breast cases and cases diagnosed before 1990.

1

Positive

2

Negative

 

PROGESTERONE STATUS

SEER*Stat Name: PR Status Recode Breast Cancer (1990+)

Field Description: Created by combining information from Tumor marker 2 (1990-2003) (NAACCR Item #=1150), with information from CS site-specific factor 2 (2004+) (NAACCR Item #=2880). This field is blank for non-breast cases and cases diagnosed before 1990.

1

Positive

2

Negative

 

REGIONAL NODES EXAMINED

SEER*Stat Name: Regional nodes examined (1988+)

Field Description: Records the total number of regional lymph nodes that were removed and examined by the pathologist.

 

REGIONAL NODES POSITIVE

SEER*Stat Name: Regional nodes positive (1988+)

Field Description: Records the exact number of regional lymph nodes examined by the pathologist that were found to contain metastases.

SURVIVAL MONTHS

SEER*Stat Name: Survival Months

Field Description: Created using complete dates, including days, therefore may differ from survival time calculated from year and month only. For more information, see http://seer.cancer.gov/survivaltime.

 

STATUS

SEER*Stat Name: Vital status recode (study cutoff used) Field Description: Any patient that dies after the follow-up cut-off date is recoded to alive as of the cut-off date.

1

Alive

2

Dead

 

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