The mission of the High Throughput Experimental Materials Database (HTEM DB) is to enable discovery of new materials with useful properties by releasing large amounts of high-quality experimental data to public. The HTEM DB contains information about materials obtained from high-throughput experiments at the National Renewable Energy Laboratory (NREL).

The raw data of the experimental sample were determined.

GRO, RNAp ChIP, mRNA expression and mRNA decay data for all genes, indicating the cluster where each genes is located, whether the gene has been previously described as cell-cycle regulated, and whether it belongs to RiBi or RP regulons. mRNA expression and decay data are from Cramer´s lab [17]. (XLSX)

This is a research data set extracted from NIST Standard Reference Database 103b and contains raw experimental data for viscosity and surface tension of binary mixtures.

The HIRENASD data produced by analyzing the experimental data is repeated on this website, for those who can not download the information in the zip format found on the primary Experimental Data page, or who wish to examine the plots of the data online.

While art is omnipresent in human history, the neural mechanisms of how we perceive, value and differentiate art has only begun to be explored. Functional magnetic resonance imaging (fMRI) studies suggested that art acts as secondary reward, involving brain activity in the ventral striatum and prefrontal cortices similar to primary rewards such as food. However, potential similarities or unique characteristics of art-related neuroscience (or neuroesthetics) remain elusive, also because of a lack of adequate experimental tools: the available collections of art stimuli often lack standard image definitions and normative ratings. Therefore, we here provide a large set of well-characterized, novel art images for use as visual stimuli in psychological and neuroimaging research. The stimuli were created using a deep learning algorithm that applied different styles of popular paintings (based on artists such as Klimt or Hundertwasser) on ordinary animal, plant and object images which were drawn from established visual stimuli databases. The novel stimuli represent mundane items with artistic properties with proposed reduced dimensionality and complexity compared to paintings. In total, 2,332 novel stimuli are available open access as “art.pics” database at https://osf.io/BTWNQ/ with standard image characteristics that are comparable to other common visual stimuli material in terms of size, variable color distribution, complexity, intensity and valence, measured by image software analysis and by ratings derived from a human experimental validation study [n = 1,296 (684f), age 30.2 ± 8.8 y.o.]. The experimental validation study further showed that the art.pics elicit a broad and significantly different variation in subjective value ratings (i.e., liking and wanting) as well as in recognizability, arousal and valence across different art styles and categories. Researchers are encouraged to study the perception, processing and valuation of art images based on the art.pics database which also enables real reward remuneration of the rated stimuli (as art prints) and a direct comparison to other rewards from e.g., food or money.Key Messages: We provide an open access, validated and large set of novel stimuli (n = 2,332) of standardized art images including normative rating data to be used for experimental research. Reward remuneration in experimental settings can be easily implemented for the art.pics by e.g., handing out the stimuli to the participants (as print on premium paper or in a digital format), as done in the presented validation task. Experimental validation showed that the art.pics’ images elicit a broad and significantly different variation in subjective value ratings (i.e., liking, wanting) across different art styles and categories, while size, color and complexity characteristics remained comparable to other visual stimuli databases.

coupling effects

experimental data from the MFA algorithm

Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

This resource contains the experimental data that was included in tecplot input files but in matlab files. dba1_cp has all the results is dimensioned (7,2) first dimension is 1-7 for each span station 2nd dimension is 1 for upper surface, 2 for lower surface. dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2) Unsteady CP is dimensioned with 4 columns 1st column, real 2nd column, imaginary 3rd column, magnitude 4th column, phase, deg M,Re and other pertinent variables are included as variables and also included in casedata.M, etc

This data set is a collection of data sets and model checkpoint in the iPXRDnet.

Model checkpoint file(model.zip):
hmof-130T_Hydrogen: Model of adsorption prediction for H2 in the hMOF-130T database obtained by training
hmof-130T_CarbonDioxide: Model of adsorption prediction for CO2 in the hMOF-130T database obtained by training
hmof-130T_Nitrogen: Model of adsorption prediction for N2 in the hMOF-130T database obtained by training
hmof-130T_Methane: Model of adsorption prediction for CH4 in the hMOF-130T database obtained by training
hmof-300T: Adsorption prediction model in the hMOF-300T database obtained by training
Gas_Se: Separation selectivity prediction model obtained by training
Gas_SD: Self-diffusion coefficients prediction model obtained by training
MOD: Bulk modulus and shear modulus prediction model obtained by training
exAPMOF-1bar-ALM+PXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD and material ligands
exAPMOF-1bar-ALM: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with material ligands only
exAPMOF-1bar-PXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD only
exAPMOF-ISO:Experimental adsorption isotherm model of Anion-pillared MOFs obtained by training
exAPMOF-1bar-NOacvPXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD data before activation only
exAPMOF-1bar-acvPXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD data after activation only

Checkpoint file for model without co-learning strategy(model-No-co-learning.zip):
hmof-130T_Hydrogen: Model of adsorption prediction for H2 in the hMOF-130T database obtained by training
hmof-130T_CarbonDioxide: Model of adsorption prediction for CO2 in the hMOF-130T database obtained by training
hmof-130T_Nitrogen: Model of adsorption prediction for N2 in the hMOF-130T database obtained by training
hmof-130T_Methane: Model of adsorption prediction for CH4 in the hMOF-130T database obtained by training
hmof-130T-str: Structural characteristics prediction model in the hMOF-130T database obtained by training
hMOF-130T-GradCAM: Model for GradCAM in the hMOF-130T database obtained by training
hmof-300T: Adsorption prediction model in the hMOF-300T database obtained by training
hmof-300T-str: Structural characteristics prediction model in the hMOF-300T database obtained by training
Gas_Se: Separation selectivity prediction model obtained by training
Gas_SD: Self-diffusion coefficients prediction model obtained by training
MOD: Bulk modulus and shear modulus prediction model obtained by training

Data sets file (data.zip):
hmof-xrd+str+ad :PXRD and gas adsorption and structural feature of hmof-300T database
hMOF-130T_ad_list_mof :Gas adsorption data of hmof-130T database
hMOF-130T_GAS_DICT :Gas descriptors data of hmof-130T database
hMOF-130T_STR_DICT :Structural feature data of hmof-130T database
hMOF-130T_PXRD_DICT :PXRD data of hmof-130T database
MOD_data :Bulk modulus and shear modulus data of Moghadam's MOFs
MOD_PXRD_dict : PXRD data of Moghadam's MOFs
GAS_SD-data : self-diffusion coefficients data in CoREMOF database
SE-CO2,N2_data:Separation selectivity ,PXRD and structural feature of CO2/N2 selectivity database
Sa_sp:Data set partitioning results of CO2/N2 selectivity database
gas_dict : gas descriptors data used in the self-diffusion coefficients database
PXRD_DICT : PXRD data after activation of MOFs in Anion-pillared MOFs' experimental database
xrd_noacv : PXRD data before activation of MOFs in Anion-pillared MOFs' experimental database
Smiles_ads : Smiles data of gas in Anion-pillared MOFs' experimental database
all_exAPMOF-1bar : Anion-pillared MOFs' experimental adsorption data under 298K and 1 bar.
all_exAPMOF-1bar-NOacv : Experimental adsorption data for anion-pillared MOFs with PXRD before activation under 298K and 1 bar.
exAPMOF_DICT : Anion-pillared MOFs' Smiles data of MOFs' ligands and descriptors of metal centers in the experimental database
all_exAPMOF-iso : Key library of MOF and gas combinations in Anion-pillared MOFs' experimental isotherm database.
exAPMOF_ISOdata: Anion-pillared MOFs' experimental adsorption isotherm data under 298K.

New Data sets file (data_new.zip):

Files starting with ‘4gas_’: Files are named in the form of ‘4gas_Gas_Pressure’, recording the adsorption amounts of 20,000 randomly selected structures from the hMOF-130T database at different pressures at 298K.
all_adinfo_list_robustness: Gas adsorption data file used for study of model robustness .
hmof-130T_Xnm: PXRD data of structures in the hmof-130T database at different crystal sizes.
hMOF-130T_ad_list_mof: Corrected Gas adsorption data of hmof-130T database . There are problems with the data in the data.zip file.
X_PXRD,AD: Adsorption amount and PXRD data of ionic MOFs (iMOFs), zeolitic imidazolate frameworks (ZIF) and Uio-66 series from experimental literature. Among them, the Uio-66 series uses CO2 as the gas, and other files contain adsorption data of different gases.

This database contains the results of 146 experimental evacuation drills. Several configurations are proposed: from a single room to a multi-compartment configuration. For each test, a unique file contains all the evacuation times in seconds from the drill start when people go through the doorways. These raw data will be handled by future users to calibrate or validate their own evacuation models.

This database summarizes 165 experimental test data on beam-to-column connections for composite special moment frames (C-SMFs).

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 30 kW off-the-shelf grid following battery inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.9 to 1.1 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59 Hz to 61 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

in high flow velocity areas like those suitable for tidal applications, turbulence intensity is high and flow variations may have a major impact on tidal turbines behaviour. large boils that can be observed at the sea surface are emitted from the sea floor and may interact with the tidal turbine. these boils have then to be characterized. the reynolds number, based on the rugosity height and mean flow velocity, is rather high in this context: re = 2.5 × 107 . for that purpose, experiments are carried out in a flume tank with re as high as achievable in froude similitude (in the tank: re = 2.5 × 105 and fr = 0.23) in order to study coherent flow structures emitted behind seabed obstacles. the obstacle is here a canonical square wall-mounted cylinder chosen to be representative of specific in-situ bathymetric variations. using piv and ldv measurements, the flow past the cylinder is investigated. the database created is presented in this report.

Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials

Background

This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.

The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).

Usage

• The data is licensed through the Creative Commons Attribution 4.0 International.
• If you have used our data and are publishing your work, we ask that you please reference both:
  1. this database through its DOI, and
  2. any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.

Included Files

• Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.
• Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.
• Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data
  • Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.
  • We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Clean_Data_v1-0-0.zip: contains all the downsampled data
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Database_References_v1-0-0.bib
  • Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files.

File Format: Downsampled Data

These are the "LP_

• The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data
• Time[s]: time in seconds since the start of the test
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: the surface temperature in degC

These data files can be easily loaded using the pandas library in Python through:

import pandas
data = pandas.read_csv(data_file, index_col=0)

The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.

File Format: Unreduced Data

These are the "LP_

• The first column is the index of each data point
• S/No: sample number recorded by the DAQ
• System Date: Date and time of sample
• Time[s]: time in seconds since the start of the test
• C_1_Force[kN]: load cell force
• C_1_Déform1[mm]: extensometer displacement
• C_1_Déplacement[mm]: cross-head displacement
• Eng_Stress[MPa]: engineering stress
• Eng_Strain[]: engineering strain
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: specimen surface temperature in degC

The data can be loaded and used similarly to the downsampled data.

File Format: Overall_Summary

The overall summary file provides data on all the test specimens in the database. The columns include:

• hidden_index: internal reference ID
• grade: material grade
• spec: specifications for the material
• source: base material for the test specimen
• id: internal name for the specimen
• lp: load protocol
• size: type of specimen (M8, M12, M20)
• gage_length_mm_: unreduced section length in mm
• avg_reduced_dia_mm_: average measured diameter for the reduced section in mm
• avg_fractured_dia_top_mm_: average measured diameter of the top fracture surface in mm
• avg_fractured_dia_bot_mm_: average measured diameter of the bottom fracture surface in mm
• fy_n_mpa_: nominal yield stress
• fu_n_mpa_: nominal ultimate stress
• t_a_deg_c_: ambient temperature in degC
• date: date of test
• investigator: person(s) who conducted the test
• location: laboratory where test was conducted
• machine: setup used to conduct test
• pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control
• pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control
• pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control
• citekey: reference corresponding to the Database_References.bib file
• yield_stress_mpa_: computed yield stress in MPa
• elastic_modulus_mpa_: computed elastic modulus in MPa
• fracture_strain: computed average true strain across the fracture surface
• c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass
• file: file name of corresponding clean (downsampled) stress-strain data

File Format: Summarized_Mechanical_Props_Campaign

Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,

tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv',
          index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1],
          keep_default_na=False, na_values='')

• citekey: reference in "Campaign_References.bib".
• Grade: material grade.
• Spec.: specifications (e.g., J2+N).
• Yield Stress [MPa]: initial yield stress in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign
• Elastic Modulus [MPa]: initial elastic modulus in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign

Caveats

• The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each:
  • A500
  • A992_Gr50
  • BCP325
  • BCR295
  • HYP400
  • S460NL
  • S690QL/25mm
  • S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm

A new, relational database to be used for disease gene discovery, gene annotation and reporting, and searching for genes for future studies in model organisms. It incorporates 5 layers of information about the genes residing in it- the expression information from a gene (as reported in Unigene), the cytological location of the gene (if available), the ortholog of each gene in the available species within the database, the divergence information between species for each gene, and functional information as reported by OMIM and the Enzyme Commission (EC) reference number of genes. Tables have also been created to help record polymorphism data and functional information about specific changes within or between species, such as measured by Granthams distance (1) or model organism studies.

Data.zip contains the final data. Scripts.zip contains the analysis data.

The experiment ROCOM E2.3 represents a boron dilution event in a KONVOI-type pressurized lightwater reactor. It was conducted at room temperature with de-mineralized water without boric acid. Underborated water slugs were modelled by adding Ethanol in order to adjust a density difference of 1.22% with respect to the regular coolant inventory. At the beginning of the experiment, the slugs are enclosed between two valves in the cold legs of loops 1 and 2. The volume of the two water slugs accounts for 0.0576 m 3 (57.6 l) each and the slug fronts are located at 1.8 m upstream of the pressure vessel inlet nozzles. The experiment is started by opening the loop valves and running up the circulation pumps. The time dependency of the volumetric flow rates in all four coolant loops can be found in https://doi.org/10.1016/j.nucengdes.2020.110776. During the experiment, the mixing process was recorded by wire-mesh conductivity sensors at various positions within the coolant loops and the pressure vessel. The nomenclature of the data files as well as the format of the tables are described in the accompanying document DataDescription_ROCOME23.pdf.

An extensive precipitation database has been developed over the past 35 years with the first records starting in January 1962 and going through September 1996 from the Reynolds Creek Experimental Watershed located near the north end of the Owyhee Mountains in southwest Idaho. Precipitation ranges from 236 mm on the lowest elevations at the north end of the watershed to 1123 mm at the southwest corner of the watershed. The gauge network was changed in 1967-1968 from a single unshielded, universal-recording gauge at each location to the dual-gauge system that is presently used. The dualgauge system consists of an unshielded and a shielded universal-recording gauge with orifices 3.05 m above the ground. The number of dual-gauge sites was reduced from the original 46 in 1968 to 17 by 1996. Also, several sites have been added and/or taken out of the network at various times for special studies. There are continuous 35 year records available for 12 sites, 20-32 year records available for 8 sites, 10-19 year records available for 25 sites, and 4-9 year records for 8 sites for a total of 53 sites. All of these data have been stored as breakpoint and hourly records in the USDA-ARS, Northwest Watershed Research Center database. These breakpoint and hourly data are available. Resources in this dataset:Resource Title: Precipitation. File Name: precipitation.zipResource Description: Precipitation data