Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"

This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a> The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters: <ul> <li> cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs. </li> <li> seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number. </li> <li> EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface. </li> <li> ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970). </li> <li> fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages. </li> <li> port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved. </li> <li> freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message. </li> <li> toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size. </li> <li> dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate. </li> <li> ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic. </li> <li> gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW. <ul> <li> rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>]. </li> <li> snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level. </li> <li> ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970). </li> <li> tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity. </li> <li> time—UTC of the received message, with microsecond precision in the ISO 8601 format. </li> <li> gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW. </li> <li> lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity. </li> <li> lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity. </li> </ul> </li> <li> bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%). </li> <li> data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements. </li> <li> device_Lat—Latitude of the measurement point gathered from the GPS. </li> <li> device_Lon—Longitude of the measurement point gathered from the GPS. </li> </ul> The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.

opencc-by-4.0Feb 2022View details →

HYDRO-CSI, Project 1.2: In-stream hydrology. Part 1: Groundwater measurements

The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes. In this project we investigated groundwater and stream interactions in order to characterize the physical processes that control the water and solute exchange in the river corridor and their variability over time. To do so, we drilled 36 wells in the near-stream domain, and 7 piezometers in the stream channel. We observed the water level and electrical conductivity every 15 min at 22 of the 36 wells with a water level sensor (Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS) over a period of 32 months (July 2018 - March 2021). The dataset includes the following files: > "Raw groundwater measurements.xlsx"  This file includes groundwater table elevation measured as depth from the upper limit of the well (time step of 15 minutes, Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS). Every excel file includes also Electrical Conductivity measurements (μS/cm) and Voltage (V) of the instruments. Every sheet in this .xlsx file reports measurement for one sensor in the specific observation-well where it was placed. > "Groundwater elevation data - wells metadata and fixed groundwater table elevation.xlsx" This file includes the raw groundwater table measurements measured via the OTT, information on the well network, elevation and location of the observation wells, location of subsurface layers, and suggested correction of the groundwater table measurements for short periods with missing data. > "Hand-measurements and metadata.xlsx" This file includes the list of in-situ inspections and hand-measurements of the groundwater table conducted over the entire observation period in every well and piezometer of the groundwater-monitoring well network. Every sheet includes details on the instruments measuring the groundwater table, their offset with hand-measured data, information on their re-calibration, and calculation of the groundwater elevation above the reference plane after each hand-measurement.

Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"

Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a> Data is available in tab delimited format (.txt) and in Excel (.xls).

opencc-by-4.0Jun 2021View details →

Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files

The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository. We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses. The index scores of each jurisdiction are made available for download in the dataset: habitat_protection_indexes_average.csv The habitat specific index scores for each jurisdiction are made available for download in the dataset: habitat_protection_indexes.csv.  Column name descriptions are available in the text file: Column_name_descriptions_20220301 The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script 01_Workflow sources the first 9 scripts in the scripts folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript. A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.

Using snapshot measurements to identify high-emitting vehicles

This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022. Material for reproducing figures in the paper <ul> <li>R script: plot.r</li> <li>Data for plot 2: <ul> <li>RS_Zurich_data.csv: the sample RS data from Zurich.</li> <li>algorithm_eu5d_final_iteration.rds: the estimated average emission factor for each city fleet (outputs from the iterative algorithm)</li> </ul> </li> <li>Data for plot 3:        <ul> <li>Zurich_clean_identification.xlsx: summary of the fraction of clean vehicles being identified by each potential RS threshold.        </li> <li>Zurich_high_emitter_identification.xlsx: summary of the fraction of high-emitters being identified by each potential RS threshold.</li> </ul> </li> <li>Data for plot 4: <ul> <li>validation_test_dataset.csv: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions.</li> <li>validation_algorithm_results.rds: algorithm outputs when applied to the validation dataset.</li> </ul> </li> </ul> The iterative algorithm and sample data that can be used for demonstration <ul> <li>Algorithm script: iterative_algorithm.r</li> <li>Sample RS data: RS_Zurich_data.csv</li> <li>Sample PEMS/Chassis test cycles: sample_pems_chassis_cycles.csv</li> </ul>

Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019)

Rates of N2 fixation were measured using the 15N2 isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles.  The 15N2 gas was first dissolved into seawater and 100 mL of the resulting 15N2-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of 15N2-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the 15N2 tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S).

Data Analysis files for "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity"

Data Analysis for the paper "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity", which is published in PRX Quantum 3, 020309 (2022).

opencc-by-4.0Apr 2021View details →

Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures

This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.

opencc-by-4.0Apr 2022View details →

The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements

This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.

opencc-by-4.0Apr 2022View details →

Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis

MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.

opencc-by-4.0Apr 2022View details →

The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts

This dataset contains the joint toque measurements of a robot manipulator (<a href="https://blog.robotiq.com/bid/64944/Collaborative-Robot-Series-KUKA-s-Light-Weight-Robot-4">KUKA LWR4+</a>) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at <a href="https://www.ce.cit.tum.de/en/lsr/home/">Chair of Automatic Control Engineering</a>, <a href="https://www.tum.de/en/">Technical University of Munich</a>, Munich, Germany, by <a href="https://sites.google.com/view/zengjie-zhang/home">Dr. Zengjie Zhang</a>, under the supervision of <a href="https://www.ce.cit.tum.de/lsr/team/dozenten/dirk-wollherr/">Dr. Dirk Wollherr</a>, in 2017. Its detailed recording procedure is explained in the following work: [1] Zhang Z, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. IEEE Transactions on Automation Science and Engineering, 2020, 18(3): 1144-1156. The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm. The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB. This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.

opencc-by-4.0Jul 2021View details →

Data analysis source code and measurement data of chemosensor salt-responsiveness

Dataset with measurement data of salt-responsiveness of macrocyclic chemosensors and the Python source code for data analysis.

Luangwa Rift Fault Scarp Measurements

First Release associated with submission of article to EGU Solid Earth. Scarp height measurements for faults in the Luangwa Rift performed by Tess Turner for her University of Bristol MSci Earth Sciences final year project. If you use these measurements please cite the following paper in addition to this dataset: Turner, T., Wedmore, L.N.J., Biggs, J., Williams, J. Sichingabula, H.M., Kabumbu, C. Banda, K. The Luangwa Rift Active Fault Database and fault reactivation along the southwestern branch of the East African Rift. In preparation for Solid Earth These measurements were performed on SRTM data using the method outlined in <a href="https://doi.org/10.1029/2019TC005834">Wedmore et al., 2020</a>. Topographic profiles were sampled event 30 m and stacked at 120 m intervals along strike. Four faults in the Luangwa Rift have been measured using this technique: The Chipola, Chitembo, Kabungo and Molaza faults. The traces of these faults, and all other known active faults in the Luangwa Rift have been separately archived as part of the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database">Luangwa Rift Active Fault Database</a>. Data Format Files are provided in comma separated value (<a href="https://datatracker.ietf.org/doc/html/rfc4180">csv</a>) format. Each file contains one header line with descriptions of the data contained within each column. The column headings and extra information are summarised in the table below. Where data columns #5-10 are blank, this is because no measurements were possible in the profile corresponding to that particular row number. If columns 11-16 are blank, this is because there are no scarp height measurements within the sampling window of the moving average. Attribute Table <table align="left"> <caption>Attribute Table for the measurements of scarp height in the Luangwa Rift</caption> <thead> <tr> <th scope="col">Column #</th> <th scope="col">Attribute</th> <th scope="col">Units</th> <th scope="col">Data Type</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Longitude</td> <td>decimal degrees</td> <td>Float</td> <td> </td> </tr> <tr> <td>2</td> <td>Latitude</td> <td>decimal degrees</td> <td>Float</td> <td> </td> </tr> <tr> <td>3</td> <td>Distance Along Fault</td> <td>kilometers</td> <td>Float</td> <td> </td> </tr> <tr> <td>4</td> <td>Distance Along Fault</td> <td>meters</td> <td>Integer</td> <td> </td> </tr> <tr> <td>5</td> <td>Scarp Height</td> <td>meters</td> <td>Float</td> <td>Mean scarp height measurement of 10,000 iterations of scarp height with varying subset of points in the hanging wall, scarp and footwall slopes.</td> </tr> <tr> <td>6</td> <td>Scarp Height Standard Deviation</td> <td>meters</td> <td>Float</td> <td>Standard deviation of 10,000 iterations of meausuring the scarp height with varying subset of points in the hanging wall, scarp and footwall slopes.</td> </tr> <tr> <td>7</td> <td>Upper Slope Angle  </td> <td>degrees</td> <td>Float</td> <td>Mean upper slope dip angle of 10,000 iterations of subset of points selected from the footwall slope (above the top of the fault scarp).</td> </tr> <tr> <td>8</td> <td>upper Slope Angle Standard Deviation</td> <td>degrees</td> <td>Float</td> <td>Standard devation of upper slope angle of 10,000 random subsets of the points selected on the upper slope of the fault.</td> </tr> <tr> <td>9</td> <td>Lower Slope Angle</td> <td>degrees</td> <td>Float</td> <td>Mean lower slope dip angle of 10,000 iterations of subset of points selected from the hanging wall slope.</td> </tr> <tr> <td>10</td> <td>lower Slope Angle Standard Deviation</td> <td>degrees</td> <td>Float</td> <td>Standard devation of lower slope angle of 10,000 random subsets of the points selected on the lower slope of the fault.</td> </tr> <tr> <td>11</td> <td>Filtered median offset (1 km)</td> <td>meters</td> <td>Float</td> <td>median scarp height over 1 km  of the distance along strike (0.5 km either side of the point).</td> </tr> <tr> <td>12</td> <td>filtered standard deviation (1km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 1 km  of the distance along strike (0.5 km either side of the point).</td> </tr> <tr> <td>13</td> <td>Filtered median offset (3 km) </td> <td>meters</td> <td>Float</td> <td>median scarp height over 3 km  of the distance along strike (1.5 km either side of the point).</td> </tr> <tr> <td>14</td> <td>filtered standard deviation (3km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 3 km  of the distance along strike (1.5 km either side of the point).</td> </tr> <tr> <td>15</td> <td>Filtered median offset (5 km)</td> <td>meters</td> <td>Float</td> <td>median scarp height over 5 km  of the distance along strike (2.5 km either side of the point).</td> </tr> <tr> <td>16</td> <td>filtered standard deviation (5km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 5 km  of the distance along strike (2.5 km either side of the point).</td> </tr> </tbody> </table>                                                 Version Control This release is archived as part of the publication Turner et al. (submitted to EGU Solid Earth). It is intended that this database will be updated in the future as high resolution topography products become available and/or methods for measuring fault scarps improve. Please contact Luke Wedmore <<a href="mailto:luke.wedmore@bristol.ac.uk?subject=Luangwa%20Rift%20Fault%20Scarp%20Measurements%20Zenodo%20Upload">luke.wedmore@bristol.ac.uk</a>> for more information or if you spot any errors. References Wedmore, L.N.J., Biggs, J., Williams, J.N., Fagereng, Å., Dulanya, Z., Mphepo, F., Mdala, H. (2020) Active Fault Scarps in Southern Malawi and Their Implications for the Distribution of Strain in Incipient Continental Rifts. _Tectonics_, 39(3), e2019TC005834, <a href="https://doi.org/10.1029/2019TC005834">doi.org/10.1029/2019TC005834</a>

Neutron spin echo and intramolecular FRET and DEER-EPR measurements on hGBP1 (human guanylate binding protein 1)

Neutron spin echo (NSE), double electron–electron resonance (DEER) EPR, ensemble time-correlated single photon counting (eTCSPC) fluorescence, and single-molecule detection (SMD) fluorescence spectroscopy data of the human guanylate binding protein 1 (hGBP1). CSH prepared samples for smFRET and performed protein activity assays.  TV prepared sampled for EPR measurements. TOP, CSH, and AV performed the smFRET measurements under the supervision of CAMS. TOP analyzed the smFRET measurements. JPK performed and analyzed the EPR measurements.

Driving Events Dataset: a smartphone inertial measurement unit for driving events

The experiments were carried out by a single driver on three trips (i.e., trips #1, #2, #3) using a 2010 Volkswagen Fox 1.0 under conditions of dry track and regular asphalts. The data were collected with a smartphone model Xiaomi Redmi Note 8 Pro. We obtained 169 events, subdivided into 26 non-aggressive events, 25 aggressive right-turn events, 23 aggressive left-turn events, 29 aggressive lane change events to the right, 23 aggressive lane change events to the left, 22 aggressive braking events, and 21 aggressive acceleration events.

Pre-Preg (PP) Manufacturing and Spring-in monitoring through FBGs, DCs and 3D CMM measurements

ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials. These distortions might render otherwise qualitative components unusable due to their final geometry. Objectives of the Numerical model validation are: <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements. In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion and oven cured Pre-Preg (PP). For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon  was curved panel with a 1475 mm radius (with edge rise of 7,65 mm)  that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing. In this dataset, the data obtained from the FBGs, DCs and 3D CMM meassurements  during a PP manufacturing process and spring-in distortions monitoring can be found.

Building and characterizing a fluorescence setup to measure very low concentrations of analytes/biomarkers

This training report is the result of my internship in the B-Phot Brussels Photonics team of the VUB that took place between February 3 and April 3, 2020. The project of this internship nds its context in the European SensApp project which regroups several European research institutes and universities, including the VUB. The goal of this project is to develop a method to diagnose the Alzheimer's disease in a faster and non-invasive manner, simply through a blood test, which is currently not possible because the concentration of biomarkers of the Alzheimer's disease in the blood is too low. During this internship, I was lead to build, align and calibrate a uorescence detection setup. Using this setup, I made mea- surements of the uorescence intensity of low concentrations of dye solutions. From those measurements, I performed calculations of the signal to noise ratio in order to determine the limit of detection of the setup. Finally, I studied the kinetics of photobleaching in order to get to a better understanding of its impact on the measurements.

Temperature measurements from the SMS Gazelle, Valdivia, and SMS Planet in the Indian Ocean

This dataset contains digitized temperature records from the SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907) observations in the Indian Ocean. The data is described in: Wenegrat, J.O., E. Bonanno, U. Rack, and G. Gebbie, 2022: A century of observed temperature change in the Indian Ocean. Geophys. Res. Letters. doi:10.1029/2022GL098217. Data was digitized from the original cruise reports using independent double-entry, and checked for consistency. A number of observations were discarded due to data problems, as described in Wenegrat et al. 2022 (see also associated code repository doi:10.5281/zenodo.6646645).

ASHRAE 1836-RP main list of energy efficiency measures

Energy Efficiency Measures (EEMs) play a central role throughout the building energy efficiency industry, and lists of EEMs therefore exist in a variety of resources. However, each of these use different conventions for describing and organizing measures, which presents a major challenge for aggregating information across these resources.  The ASHRAE 1836-RP main list of energy efficiency measures was assembled as part of ASHRAE Research Project 1836 in order to discover trends in how existing resources describe and organize EEMs.  Analysis of this dataset supported the overall objective of 1836-RP, which was to develop a standardized system for the categorization and characterization of EEMs. The dataset contains the complete list of 3,490 EEMs assembled and analyzed as part of 1836-RP. The EEMs were collected from 16 different source documents during the 1836-RP literature review from September 2019 through July 2020. An initial list of suggested sources was provided by the members of the 1836-RP Project Advisory Board, and additional documents were added through the authors’ literature review. A data dictionary can be found in the README.txt file.  Additional information on working with this dataset can be found in the project repository: <a href="https://github.com/retrofit-lab/ashrae-1836-rp-text-mining">https://github.com/retrofit-lab/ashrae-1836-rp-text-mining</a>

iCE40 Bitstream Size Reduction - Bitstreams and Reconfiguration Time Measurements

The HDF5 file contains data from an experiment concerning bitstream size reduction for Lattice iCE40 FPGAs. Five projects were synthesized with two different toolchains and afterwards compacted and compressed. Two compaction levels were applied to each of the ten original bistreams, resulting in a total of 30 bitstreams. The ten original bitstreams were also compressed with two different compression tools, resulting in 20 compressed versions. The reconfiguration time for each of the 30 bitstreams was measured 10.000 times. Toolchains: <ul> <li>Lattice iCEcube2</li> <li>Open-source toolchain (Yosys, nextpnr, Project IceStorm)</li> </ul> Projects: <ul> <li>blinky: A simple example design that is included in the open-source toolchain.</li> <li>ehw: An evolved design.</li> <li>attosoc: A minimal RISC-V system on a chip, that is used for tests in the open-source toolchain.</li> <li>updater: A design that receives a new configuration, decrypts it (AES) and writes it to flash.</li> <li>picosoc: RISC-V system on a chip</li> </ul> Compaction levels: <ul> <li>original: Uncompacted bitstream</li> <li>builtin: Compacted with two methods also available in iCEcube2</li> <li>compact: Compacted with all five methods</li> </ul> Compression tools: <ul> <li>icecompr: Included in Project IceStorm</li> <li>gzip: Version 1.9, compression level --best</li> </ul>   File structure: The data is organzied hierarchical. It is first split between bitstreams, measurements and compressed versions.  Bitstreams and measurements then are divided by toolchain, then by project and finally by compaction level. The compressed versions are divided by toolchain, then by project and finally by compression tool.