Comprehensive dataset of 48,456 Print shops in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Figure 1. Formulation and characterization of nanosuspension inks: SEM images of griseofulvin (GF), sodium starch glycolate (SSG), and milled nanosuspension, and TEM image of the nanosuspension. Data set: particle size distribution (vol.%) of the milled nanosuspension, water content (wt.%) as a function of incubation time at 35°C during preparation of various ink formulations, and mass of water evaporated as a function of time as well as % mass loss with temperature (from TGA).Figure 2. Rheology of ink formulations: Shear viscosity with shear rate, shear stress with shear rate, shear modulus with frequency, shear modulus with shear strain, and shear modulus with time at repetitive low (0.05%) and high (300%) strain.Figure 3. Characterization of the printability of ink formulations. B) Printed grid designs, including photographs of the scaffolds, optical images of the pores, and thresholded images of pores. D) Printed solid discs, including top-view images, cross-sectional images, and thresholded images used for contact angle calculation. F) Optical images of printed struts for the 40% ink under varying print pressures (400-600 kPa) and print speeds (3-15 mm/s) (scale bars = 500 mm). Data set: Printability index (Pr) plotted for each ink formulation, contact angle values for each ink formulation, and measured strut width for all conditions.Figure 4. Data set including change in ink flow rate (calculated and experimentally measured) with print pressure (P), experimentally measured line width and predicted line width for P = 400, 500, and 600 kPa and nozzle offset = 400, 500, and 700 mm, and experimentally measured line width with predicted values for all conditions.Figure 5. Data set: summary of machine learning outcomes: Model efficiency and validation for Gradient Boosting, K-Neigbors, Random Forest, and Linear Regressor models, inverse prediction of printing parameters from the target line width using the General Bossting Regressor model, actual line width measured for struts printed using a single set of predicted print parameters, actual line width measured for struts printed using multiple sets of predicted print parameters.Figure 6. Imaging and characterization of 3D-printed tablets alongside compressed powder mixture (PM) and griseofulvin (GF): Micro-CT images of 3D-printed tablets (using 40% ink) with 6 mm diameter (100% and 50% infill) and 9 mm diameter (50% infill), along with control PM and GF samples. Data set including XRD profiles of HPC, GF, PM, and 3D-printed dose, and DSC profiles for HPC, GF, PM, and 3D-printed dose. Figure 7. Intra-tablet homogeneity and operator effects on process quality: UV-VIS data analysis of GF content across the same layers, and as-printed weight, dried weight and GF weight (determined by UV-VIS) for 3D-printed tablets produced by operators with varying levels of expertise.Figure 8. (A) Average compressive modulus of each sample, including 3D-printed (3DP) dose, swollen 3DP dose, compressed powder mixture (PM), GF powder, and a commercial gummy. (B) Percent dissolved GF over time for 3D-printed (3DP) tablets with 100% infill (6 mm diameter), 50% infill (6 and 9 mm diameter), as well as compressed PM and GF (Data are presented as mean ± std. for n = 3).

A curated list of preprocessed & ready to use under a minute Human Activity Recognition datasets.

All the datasets are preprocessed in HDF5 format, created using the h5py python library. Scripts used for data preprocessing are provided as well (Load.ipynb and load_jordao.py)

Each HDF5 file contains at least the keys:

x a single array of size [sample count, temporal length, sensor channel count], contains the actual sensor data. Metadata contains the names of individual sensor channel count. All samples are zero-padded for constant length in the file, original lengths before padding available under the meta keys.

y a single array of size [sample count] with integer values for target classes (zero-based). Metadata contains the names of the target classes.

meta contain various metadata, depends on the dataset (original length before padding, subject no., trial no., etc.)

Usage example

import h5py

with h5py.File(f'data/waveglove_multi.h5', 'r') as h5f: x = h5f['x'] y = h5f['y']['class'] print(f'WaveGlove-multi: {x.shape[0]} samples') print(f'Sensor channels: {h5f["x"].attrs["channels"]}') print(f'Target classes: {h5f["y"].attrs["labels"]}') first_sample = x[0]

Output:

WaveGlove-multi: 10044 samples

Sensor channels: ['acc1-x' 'acc1-y' 'acc1-z' 'gyro1-x' 'gyro1-y' 'gyro1-z' 'acc2-x'

'acc2-y' 'acc2-z' 'gyro2-x' 'gyro2-y' 'gyro2-z' 'acc3-x' 'acc3-y'

'acc3-z' 'gyro3-x' 'gyro3-y' 'gyro3-z' 'acc4-x' 'acc4-y' 'acc4-z'

'gyro4-x' 'gyro4-y' 'gyro4-z' 'acc5-x' 'acc5-y' 'acc5-z' 'gyro5-x'

'gyro5-y' 'gyro5-z']

Target classes: ['null' 'hand swipe left' 'hand swipe right' 'pinch in' 'pinch out'

'thumb double tap' 'grab' 'ungrab' 'page flip' 'peace' 'metal']

Current list of datasets:

WaveGlove-single (waveglove_single.h5)

WaveGlove-multi (waveglove_multi.h5)

uWave (uwave.h5)

OPPORTUNITY (opportunity.h5)

PAMAP2 (pamap2.h5)

SKODA (skoda.h5)

MHEALTH (non overlapping windows) (mhealth.h5)

Six datasets with all four predefined train/test folds as preprocessed by Jordao et al. originally in WearableSensorData (FNOW, LOSO, LOTO and SNOW prefixed .h5 files)

Comprehensive dataset of 623 Print shops in Nevada, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Print services in Italy as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset is for the Data in Brief article: "Tensile Performance Data of 3D Printed Photopolymer Gyroid Lattices". Individual CSV files for each printed and mechanically tested lattice sample are contained in the "Raw Mechanical Data" folder, and the measured porosity values for these samples are contained in "Porosity_data.xlsx". The three-letter acronym appended to each sample name corresponds to the material used to print that sample. The full material names and printers used to print samples of each material can be found in "Materials_table.pptx". Mechanical properties for each sample were extracted from the individual stress-strain curves using a custom MATLAB script, and both the extracted properties and script are available in the "MATLAB" folder. Data and results for the finite element simulations performed in the corresponding research article for the Data in Brief article are available in "FEA_data.xlsx". For additional information about the dataset, including specific steps for how the data was obtained, see the Data in Brief article mentioned above.

This manuscript provides the first report of a fully additively manufactured (AM) electrochemical cell printed all-in-one, where all the electrodes and cell are printed as one, requiring no post-assembly or external electrodes. The three-electrode cell is printed using a standard non-conductive poly(lactic acid) (PLA)-based filament for the body and commercially available conductive carbon black/PLA (CB/PLA, ProtoPasta) for the three electrodes (working, counter, and reference; WE, CE, and RE, respectively). The electrochemical performance of the cell is evaluated first against the well-known near-ideal outer-sphere redox probe hexaamineruthenium(III) chloride (RuHex), showing that the cell performs well using an AM electrode as the pseudo-RE. Electrochemical activation of the WE via chronoamperometry and NaOH provides enhanced electrochemical performances toward outer-sphere probes and for electroanalytical performance. It is shown that this activation can be completed using either an external commercial Ag|AgCl RE or through simply using the internal AM CB/PLA pseudo-RE and CE. This all-in-one electrochemical cell (AIOEC) was applied toward the well-known detection of ascorbic acid (AA) and acetaminophen (ACOP), achieving linear trends with limits of detection (LODs) of 13.6 ± 1.9 and 4.5 ± 0.9 μM, respectively. The determination of AA and ACOP in real samples from over-the-counter effervescent tablets was explored, and when analyzed individually, recoveries of 102.9 and 100.6% were achieved against UV–vis standards, respectively. Simultaneous detection of both targets was also achieved through detection in the same sample exhibiting 149.75 and 81.35% recoveries for AA and ACOP, respectively. These values differing from the originals are likely due to electrode fouling due to the AA oxidation being a surface-controlled process. The cell design produced herein is easily tunable toward different sample volumes or container shapes for various applications among aqueous electroanalytical sensing; however, it is a simple example of the capabilities of this manufacturing method. This work illustrates the next step in research synergising AM and electrochemistry, producing operational electrochemical sensing platforms in a single print, with no assembly and no requirements for exterior or commercial electrodes. Due to the flexibility, low-waste, and rapid prototyping of AM, there is scope for this work to be able to span and impact a plethora of research areas.

Comprehensive dataset of 223 Print shops in Idaho, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Print services in Poland as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Overview The edeniss2020 dataset is a time series dataset. It consists of equidistant sensor readings stemming from 97 sensors in the EDEN ISS research greenhouse.

EDEN ISS was a (almost) closed loop research greenhouse build under the lead of the German Aerospace Center to study Controlled Environment Agriculture (CEA) techniques and plant growth for future long-term space missions. EDEN ISS was deployed in Antarctica in 2018 next to the german Neumayer III polar station and has been in operation for four years.

The data contained in the edeniss2020 dataset was recorded during the third mission year in 2020 between 2020/01/01 and 2020/12/30. Every sensor within the dataset is related to one of the following Subsystems |Acronym | Description | |--|--| | AMS‑FEG | Atmosphere Management System (AMS) of the Future Exploration Greenhouse (FEG) | | AMS‑SES | Atmosphere Management System AMS of the Service Section (SES)| | ICS | Illumination Control System | | NDS | Nutrient Delivery System | | TCS | Thermal Control System|

Specification |Item|Description | |--|--| | Number of Files | 97 | | Start Date | 2020/01/01 00:00:05 | | End Date | 2020/12/30 23:55:00 | | Sampling Rate | 1/300 Hz (5min) |

Contents The dataset includes the following files: - ams-feg/*.csv: Sensor readings related to the AMS-FEG - ams-ses/*.csv: Sensor readings related to the AMS-SES - ics/*.csv: Temperature readings related to the ICS - nds/*.csv: Sensor readings related to the NDS - tcs/*.csv: Sensor readings related to the - edeniss2020.csv: Description and Units of the measurements. - README.md: This file.

| Subsystem | Sensor | #sensors | Note | |--|--|--|--| | AMS-FEG | CO2 | 2 | | | | Photosynthetic Active Radiation (PAR) | 2 | | | | Relative Humidity (RH) | 2 | | | | Temperature (T) | 3 | | | AMS-SES | CO2 | 2 | | | Photosynthetic Active Radiation (PAR) | 1 | | | | Relative Humidity (RH) | 1 | | | | Temperature (T) | 3 | | | | Vapor Pressure Deficit (VPD) | 1 | | | ICS | Temperature (T) | 38 | Measured at the LED lamp above each growth tray | | | NDS | Electrical Conductivity (EC) | 4 | EC of the nutrient solutions | | | Level (H) | 2 | Level of the solution in the nutrient solution tanks | | | PH-Value (PH) | 4 | PH Value of the nutrient solutions | | | Pressure (P) | 8 | Pressure in the piping from the tanks to the growth racks in the FEG | | | Temperature (T) | 4 | Temperature of the nutrient solutions. | | | Volume (V) | 2 | Volume up to the level sensor | | TCS | Pressure (P) | 3 | | | Relative Humidity (RH) | 2 | | | Temperature (T) | 12 | | | Valve (VALVE) | 3 |

Usage The dataset can be used for uni- and multivariate analysis.

To read a uni-variate time series with pandas do:

import pandas as pd

Example of loading ams-feg/co2-1.csv
data = pd.read_csv('./ams-feg/co2-1.csv')
print(data.head())

To read the data for a whole subsystem (e.g. AMS-FEG) as a multivariate time series: ```python import pandas as pd import glob

SUBSYSTEM = 'ams-feg'

combined_df = pd.DataFrame() for i, file in enumerate(glob.glob(f'./{SUBSYSTEM}/*.csv')): df = pd.read_csv(file, header=0, usecols=[0, 1]) if i == 0: combined_df = df else: combined_df = pd.merge(combined_df, df, on='time')

combined_df = combined_df.sort_values(by='time').reset_index(drop=True)

print(combined_df) ```

License Creative Commons Attribution 4.0 International (CC BY 4.0 Deed)

Citation If you use this dataset, please cite it as follows: Rewicki, F., Norman, T., Vrakking, V., (2024). edeniss2020. Zenodo. http://doi.org/10.5281/zenodo.11485183

Contact Ferdinand Rewicki: ferdinand.rewicki@dlr.de

Version History

v1.0 Initial Version

Dataset Card for HumanEdit

Paper (CVPR 2025 AI for Content Creation (AI4CC) Workshop)

  Usage

from datasets import load_dataset from PIL import Image

Load the dataset

ds = load_dataset("BryanW/HumanEdit")

Print the total number of samples and show the first sample

print(f"Total number of samples: {len(ds['train'])}") print("First sample in the dataset:", ds['train'][0])

Retrieve the first sample's data

data_dict = ds['train'][0]

Save the input image (INPUT_IMG)… See the full description on the dataset page: https://huggingface.co/datasets/BryanW/HumanEdit.

Comprehensive dataset of 8,246 Print shops in Italy as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 17,434 Print shops in Mexico as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Print services in Greece as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Print services in Finland as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 742 Print shops in Greece as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 0 Print services in Switzerland as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 70 Print shops in Salamanca, Spain as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 23 Print shops in Province of Grosseto, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 12,846 Print shops in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.