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830 results for “industry”
RECODE_DS19.Toxicological profile of calcium carbonate nanoparticles for industrial applications
<p>The documentation will include: for the <em>in vitro</em> and <em>in vivo </em>studies all the data acquired after the exposure of cells or zebrafish to the nano-sized CaCO3 particles.</p>
Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
Model output from the Bern3D model of pre-industrial and Last Glacial Maximum
<p>The here presented datasets contain the model results of the Bern3D model to be published in Pöppelmeier et al. (2021) <em>Climate of the Past Discussions</em> (https://doi.org/10.5194/cp-2020-135).</p> <p>The datasets of the five performed simulations are presented: PI_CTRL, LGM_CTRL, LGM_BS, LGM_BS+wind, LGM_BS+wind+tidal. For detailed information on the model set ups please see section 2 and Table 1 of Pöppelmeier et al. (2021).</p>
An Empirical Evaluation of Video Conferencing Systems Used in Industry, Academia, and Entertainment
<p>Video Conferencing Systems~(VCS) are used daily--at work, in online education, and for get-togethers with friends and family. Many new VCSs have emerged in the past decade and a new market-leader has risen during the coronavirus period of 2020. Understanding how these systems work could help us improve them rapidly. However, no experimental comparison of such systems currently exists. </p> <p>We propose a method to compare VCSs in real-world operation and implement it as a tool. Our method considers 4 main real-world experiments. Each captures different aspects, such as communication channels~(audio, video, audio-video), types of network environments~(e.g., Ethernet, WiFi, 4G), and reports system and network utilization. We conduct real-world experiments with 3 VCSs, Zoom, Microsoft Team, and Discord.</p> <p>This dataset is the result of our experiments conducted with the Online Video Conference Benchmarking tool.</p>
An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry - Accompanying Anonymized Transcripts
<p>Anonymized interviews with 25 engineers on their experience applying Event Sourcing, with accompanying classifications. These transcripts are used in our publication "An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry".</p>
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
Improved Converted Traces from Rebasing Microarchitectural Research with Industry Traces
<p>Improved converted traces of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the CVP-1 traces used in the paper converted with our improved converter.</p><p><i>Abstract</i>: Microarchitecture research relies on performance models with various degrees of accuracy and speed. In the past few years, one such model, ChampSim, has started to gain significant traction by coupling ease of use with a reasonable level of detail and simulation speed. At the same time, datacenter class workloads, which are not trivial to set up and benchmark, have become easier to study via the release of hundreds of industry traces following the first Championship Value Prediction (CVP-1) in 2018. A tool was quickly created to port the CVP-1 traces to the ChampSim format, which, as a result, have been used in many recent works. We revisit this conversion tool and find that several key aspects of the CVP-1 traces are not preserved by the conversion. We therefore propose an improved converter that addresses most conversion issues as well as patches known limitations of the CVP-1 traces themselves. We evaluate the impact of our changes on two commits of ChampSim, with one used for the first Instruction Championship Prefetching (IPC-1) in 2020. We find that the performance variation stemming from higher accuracy conversion is significant.</p>
Bibliographic Dataset for the Systematic Literature Review on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the bibliographic information found after applying the Search Strategy used for the Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice: Systematic Literature Review. The following electronic databases were searched:</p> <ul> <li>Scopus.</li> </ul> <p>A total of 907 records were found. The search was conducted on 16/02/2024.</p> <p>The information is presented in .ris, .bib, and .csv format.</p>
The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection
<p><strong>See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a></strong></p> <p>Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified.</p> <p>On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required.</p> <p>Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the <em>Automotive Visual Inspection Dataset (AutoVI)</em>, the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines.</p> <p>The dataset contains 3950 images, split into 1530 training images and 2420 testing images.</p> <p>The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>.</p> <p><strong>Disclaimer</strong><br>All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting.</p> <p><strong>License</strong><br>Copyright © 2023-2024 Renault Group</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p> <p>For using the data in a way that falls under the commercial use clause of the license, please contact us.</p> <p><strong>Attribution</strong><br>Please use the following for citing the dataset in scientific work:</p> <p>Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a></p> <p><strong>Contact</strong><br>If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr.</p> <p><strong>Changelog</strong></p> <ul> <li><em>v1.0.0</em> <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li><em>v0.1.1</em> <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction
<p>A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.</p>
Industry 5.0 Data Model
<p>Industry 5.0 Data Model</p>
Motion Capture Benchmark of Industrial Tasks for Ergonomic Assessment and European Historic Crafts
<p><strong>General Info:</strong></p> <p>This benchmark provides motion capture (MoCap) files in .bvh form. The recordings were done in the span of May 2019 to January 2020 for the needs of the <a href="https://collaborate-project.eu/"><strong>CoLLaboratE</strong></a> and <a href="http://www.mingei-project.eu/"><strong>MINGEI</strong></a> H2020 projects<strong> </strong>funded by the European Commission. The tasks included are:</p> <ul> <li>TV assembling</li> <li>Airplane component manufacturing</li> <li>High ergonomic hazard motions </li> <li>Silk-Weaving</li> <li>Glassblowing</li> <li>Mastic Cultivation</li> </ul> <p>The TV assembly and airplane component manufacturing tasks were recorded in real-world conditions inside the factory during the actual production of the items. The high ergonomic hazard motions were recorded in a controlled lab environment and serve as baseline/prototype motions for ergonomic risk assessment.</p> <p>The silk-weaving, glassblowing, and mastic cultivation data sets were created, corresponding to movements performed by skilled craftsmen and mastic farmers. These data sets were produced in order to extract the expert's gestural knowledge and analyze their dexterity while doing their crafts.</p> <p><strong>Naming Convention:</strong></p> <p>All files in this benchmark follow a strict naming convention to allow for easier parsing by scripts. The names have a total of 12 or 13 characters that convey the following information:</p> <ul> <li>The first three or fours characters label the <strong>recording session </strong>(e.g., LAB, PLN, GBBC, MCSN, etc.)</li> <li>The next three characters label the <strong>subject number </strong>(e.g., S01, S02, S03, etc.)</li> <li>The next three characters label the <strong>posture or gesture number </strong>(e.g., P01, P02, G01, G02, etc.)</li> <li>The final three characters label the <strong>repetition number </strong>(e.g., R01, R02, R03, etc.)</li> </ul> <p>For example, LABS02P03R01 denotes a lab recording of the second subject, performing the third posture for the first time.</p> <p><strong>Recording Sessions:</strong></p> <p>There are six recording sessions in this benchmark, the ergonomic risk motion recorded in the lab (denoted as "<strong>LAB</strong>"), the construction of an airplane component (denoted as "<strong>PLN</strong>"), and the assembling and packaging of TVs (denoted as "<strong>TV*</strong>"), the silk weaving (denoted as "<strong>SW*</strong>"), glassblowing (denoted as "<strong>GB*</strong>"), and mastic cultivation (denoted as "<strong>MC*</strong>").</p> <p>The postures are the following:</p> <p><strong>LAB:</strong></p> <ul> <li><strong>Standing:</strong> <ul> <li><strong>P01</strong>: The subject stays in I-pose</li> <li><strong>P02:</strong> The subject rotates his/her torso to the left as far the person can</li> <li><strong>P03: </strong>The subject will laterally bend his/her torso to the left for 6 seconds</li> <li><strong>P04</strong>: The subject bends more than 20° but less than 60°</li> <li><strong>P05:</strong> The subject bends more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P06: </strong>The subject stretches his/her arms, and bends forward more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P07</strong>: The subject bends more than 60°</li> <li><strong>P08:</strong> The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P09: </strong>The subject stretches his/her arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P10:</strong> The subject upright, raises the elbows above the shoulder level with the forearms bent 90° (</li> <li><strong>P11</strong>: The subject raises the elbows above the shoulder level with the forearms bent 90° while rotating and laterally bending the torso to the left</li> <li><strong>P12:</strong> The subject raises the elbows above the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> <li><strong>P13:</strong> The subject upright, raises the hands above the head</li> <li><strong>P14: </strong>The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li><strong>Sitting on a chair:</strong> <ul> <li><strong>P15: </strong>The subject sits upright</li> <li><strong>P16:</strong> The subject bends forward more than 60°</li> <li><strong>P17:</strong> The subject bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P18:</strong> The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P19:</strong> The subject raises the hands above the head with arms stretched</li> <li><strong>P20:</strong> The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li><strong>Kneeling:</strong> <ul> <li><strong>P21:</strong> The subject stays upright</li> <li><strong>P22:</strong> The subject rotates the torso to the left as far he/she can</li> <li><strong>P23: </strong>The subject will laterally bend the torso to the left for 6 seconds</li> <li><strong>P24:</strong> The subject bends more than 60°</li> <li><strong>P25:</strong> The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P26:</strong> The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P27: </strong>The subject upright, raises the elbows to the shoulder level with the arms stretched</li> <li><strong>P28:</strong> The subject raises the elbows to the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> </ul> <p>The TV assembling tasks are further divided. The subtasks are: packing the TVs on a stack for shipping (denoted as "<strong>TVP</strong>" for medium-sized TVs and "<strong>TVL</strong>" for larger TVs), placing assembling and placing electronic circuit boards on the chassis (denoted as "<strong>TVB</strong>"), and screwing the boards on the TV chassis (denoted as "<strong>TV_</strong>"). Each task is comprised of a number of postures. </p> <p><strong>TV Assembling:</strong></p> <ul> <li><strong>Assembling the board and placing it on the TV chassis (TVB):</strong> <ul> <li><strong>P01: </strong>Reaching high, above the shoulder level, to pick one component</li> <li><strong>P02: </strong>Reaching low, below the knee level, to pick up the second component</li> <li><strong>P03: </strong>Connecting the components and placing the board on the chassis to be screwed</li> </ul> </li> <li><strong>Screwing an electrical circuit board on the TV chassis (TV_) :</strong> <ul> <li><strong>P01: </strong>A screw is placed on a power tool and it is being screwed on the chassis. The process is repeated four times</li> </ul> </li> <li><strong>Preparing TVs for Shipping (TVP & TVL):</strong> <ul> <li><strong>P01: </strong>Placing TVs on a wooden pallet (bottom level)</li> <li><strong>P02:</strong> Preparing to wrap the bottom level with a membrane</li> <li><strong>P03:</strong> Wrapping the bottom level</li> <li><strong>P04:</strong> Placing TVs on top of the bottom level (second level)</li> <li><strong>P05:</strong> Placing TVs on top of the second level (third level)</li> <li><strong>P06: </strong>Wrapping the second level with a plastic membrane</li> <li><strong>P07:</strong> Wrapping the third level with a plastic membrane</li> <li><strong>P08:</strong> Placing TVs on top of the third level (fourth level)</li> <li><strong>P09:</strong> Wrapping the fourth level with a plastic membrane</li> </ul> </li> </ul> <p><strong>Riveting of an airplane floater (PLN):</strong></p> <ul> <li><strong>P01:</strong> Rivet with the pneumatic hammer.</li> <li><strong>P02:</strong> Prepare the pneumatic hammer and grab rivets. </li> <li><strong>P03:</strong> Place the bucking bar to counteract the incoming rivet.</li> </ul> <p>The tasks recorded for silk weaving, glassblowing, and mastic cultivation data sets were segmented by gestures (e.g., G01, G02, etc.) . The tasks recorded for these three data sets are the following:</p> <p><strong>Silk weaving (SW*):</strong></p> <ul> <li>The creation of the punch cards <strong>(SWPC)</strong>.</li> <li>Preparation of the beam <strong>(SWPB)</strong>.</li> <li>Wrapping of the beam <strong>(SWWB)</strong>.</li> <li>Jacquard weaving with small loom <strong>(SWSL)</strong>.</li> <li>Jacquard weaving with medium size loom <strong>(SWML)</strong>.</li> <li>Jacquard weaving with large loom <strong>(SWLL)</strong>.</li> </ul> <p><strong>Glassblowing (GB*):</strong></p> <ul> <li>Beak cutting <strong>(GBBC)</strong>.</li> <li>Blowing and shaping <strong>(GBBS)</strong>.</li> <li>Cervix refining <strong>(GBCR)</strong>.</li> <li>Cord laying <strong>(GBCL)</strong>.</li> <li>Finish details <strong>(GBFD)</strong>.</li> <li>Handle laying <strong>(GBHL)</strong>.</li> <li>Transfer to punty <strong>(GBTP)</strong>.</li> <li>Leg and foot laying <strong>(GBLF)</strong>.</li> </ul> <p><strong>Mastic Cultivation (MC*):</strong></p> <ul> <li>Scrapping with new tool <strong>(MCSN)</strong>.</li> <li>Scrapping with old tool <strong>(MCSO)</strong>.</li> <li>Sweeping <strong>(MCSW)</strong>.</li> <li>Dusting <strong>(MCDU)</strong>.</li> <li>Embroidery A <strong>(MCEA)</strong>.</li> <li>Embroidery B <strong>(MCEB)</strong>.</li> <li>Embroidery with an axe <strong>(MCEX)</strong>.</li> <li>Gathering <strong>(MCGA)</strong>.</li> <li>Harvesting <strong>(MCHA)</strong>.</li> <li>Wiping <strong>(MCWI)</strong>.</li> <li>Shifting A <strong>(MCSA)</strong>.</li> <li>Shifting B <strong>(MCSB)</strong>.</li> <li>Cleaning with the wind <strong>(MCCW).</strong></li> </ul> <p>The motion capture files were processed and segmented with a 3D character animation software (MotionBuilder, Autodesk Inc., San Rafael, CA. USA) and exported to Biovision Hierarchy (BVH) files.</p>
Replication data for: Energy flow analysis of an industrial ammonia refrigeration system
<p>This dataset includes energy data acquired from a pelagic fish processing plant, including data from an industrial ammonia refrigeration system that provides cooling and freezing. In addition, production data is included. Data from the system was analysed within the KSP project PCM-STORE (308847) supported by the Research Council of Norway and industry partners. PCM-STORE aims at building knowledge on novel PCM technologies for low-temperature thermal energy storage. Collecting and analysing data is an important part of evaluating the potential for reduction of CO2 emissions and increasing energy efficiency. Many processing plants measure and log data, but it is not often published. This dataset includes specific energy demand, peak power demand, power demand for different sections of the plant, ambient temperatures, and production volumes. The data was collected in 2021. The included graphics show the refrigeration system and some resulting tables and graphs. Production follows a seasonal cycle throughout the year, with no (or very low) production in the spring (Mar-May), and peak production in the autumn (Sep-Nov). The cycle is linked to the seasonal availability of fish. Annual SEC numbers (200-247 kWh/tonnes) were found to be in line with other Norwegian pelagic plants. A strong dependency between SEC and volume throughput were also found, where months of low production resulted in high SEC values and vice versa. Knowledge about the processes indicates that a fillet production is more energy intensive compared to round production, due to more energy demand from the fillet sections, higher mass (fish and brine) in each box and higher requirement of hot water for cleaning. This dataset is related to the conference paper "Energy flow analysis of an industrial ammonia refrigeration system and potential for a cold thermal energy storage" presented at the 15th IIR Gustav Lorentzen Conference on Natural Refrigerants, Trondheim, Norway 13-15 June 2022.</p>
An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]
<p>Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions. </p> <p>Data format: *.opj; *.tif.</p> <p>Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers
<p>The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.</p> <p>The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/</p> <p>The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Data_Torrefaction of pulp industry sludge to enhance its fuel characteristics
<p>Recently, under COP26, several countries agreed to phase-out coal from their energy systems. Torrefaction industry can take advantage of this, as the fuel characteristics of the torrefied biomass are comparable to those of coal. However, in terms of economic feasibility, torrefied biomass pellets are not yet competitive with coal without subsidies because of the high price of woody biomass. Thus, there is a need to produce torrefied pellets from low-cost feedstock, and pulp industry sludge is one such feedstock. In that context, this study was focused on torrefaction of pulp industry sludge. Torrefaction experiments were carried out using a continuous reactor, at temperatures 250, 275, and 300 ℃. The heating value of the sludge increased from 19 to 22 MJ/kg after torrefaction at 300 ℃. The fixed carbon content increased from 16 wt.% for dried pulp sludge to 30 wt.% for torrefied pulp sludge. The fuel ratio was in the range of 0.27 to 0.61. The ash content of the pulp sludge is comparable to the agricultural waste, which is around 12 wt.% (dry basis). The cellulose content in the sludge reduced from 35 to 12 wt.% at 300 ℃. The ash related issues such as slagging, fouling and bed agglomeration tendency of the sludge were in the medium range. This study shows that torrefaction treatment can improve the fuel properties of the pulp industry sludge to a level comparable to that of low-rank coal.</p>
Data Set for_Integrating torrefaction of pulp industry sludge with anaerobic digestion to produce biomethane and volatile fatty acids: An example of industrial symbiosis for circular bioeconomy
<p>Industrial symbiosis, which allows the sharing of resources between different industries, could help to improve the overall feasibility of bio-based chemicals production. In that regard, this study focused on integrating the torrefaction of pulp industry sludge with anaerobic digestion. More specifically, anaerobic digestion (AD) of pulp sludge-derived torrefaction condensate (TC) was studied to evaluate the biomethane and volatile fatty acid (VFA) potential. The torrefaction condensate produced at 275 and 300 °C was used in AD. The volatile solid content (VS) was 6.69 and 9.01% for the condensate produced at 275 and 300 °C, respectively. The organic fraction of TC mainly contained acetic acid, 2-furanmethanol, and syringol. The methane yield was in the range of 481–772 mL/g VS for the mesophilic and 401–746 mL/g VS for the thermophilic process, respectively. The VFA yield was in the range of 1.1 to 3.4 g/g VS for mesophilic and from 1.5 to 4.7 g/g VS in thermophilic conditions, when methanogenesis was inhibited. Finally, pulp sludge TC is a feasible feedstock to produce platform chemicals like VFA. However, at higher substrate loading, signs of process inhibition were observed because of the relatively increasing concentration of microbial inhibitors</p>
TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant
<p>Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves). </p> <p>Data format: *.opj; *.tif.; *docx; *jpg</p> <p>Software needed: Origin.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.