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3,443 results for “technology”
Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Data for "Systematic Mapping of Open Data Studies: Classification and Trends from a Technological Perspective"
<p>Data used to perform a systematic mapping to classify and analyse existing research on open data from a technological viewpoint from 2006 to 2019. This dataset contains information from six key facets from the collected publications coming from several scientific repositories/databases: publication venue, impact, subject, domain, life-cycle and research type.</p>
A Non-galvanic D-band MMIC-to-Waveguide Transition Using eWLB Packaging Technology-dataset
<p>This paper presents a novel D-band interconnect implemented in a low-cost embedded Wafer Ball Grid Array (eWLB) commercial process. The transition is realized through a patch slot antenna directly radiating to a standard waveguide opening. The interconnect achieves low insertion loss and good bandwidth. The measured minimum Insertion Loss (IL) is 2 dB and the average is 3 dB across a bandwidth of 22% covering the frequency range 110-138 GHz. In addition, the structure is easy to integrate as it does not require any special assembly nor any galvanic contacts. Adopting the low-cost eWLB process and standard waveguides makes the transition an attractive solution for interconnects beyond 100 GHz.</p>
PUDL Raw NREL Annual Technology Baseline (ATB) for Electricity and Transportation
<p>The NREL Annual Technology Baseline (ATB) for Electricity publishes annual projections of operational and capital expenditures (by technology and vintage), as well as operating characteristics (by technology). Archived from <a href="https://atb.nrel.gov/">https://atb.nrel.gov/</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></p>
What are the key tensions in educational technology (Edtech)?
<p>This video outlines the key challenges and tensions that have arisen in the higher education sector as it increasingly operates using digital technology, and how these can be used to direct future improvements of digital processes in higher education. The findings come from the ESRC-funded project 'Universities and Unicorns: building digital assets in the higher education industry'.</p>
e-DIPLOMA - Dataset: The values of using disruptive technologies
<p>This is the dataset "The values of using disruption technologies" of the e-DIPLOMA project and includes the following documents:</p> <ul> <li>Values.xlsx</li> <li>Scenarios.ppt</li> <li>ReadmeFile.rtf</li> </ul> <p>The data were collected in the values-workshop. They describe how the practice based example learning scenarios with disruptive technologies are perceived regarding values. The data were collected in workshops held in several European countries. The participants could read about the learning design scenarios with disruptive technologies and then discuss the values they perceived regarding these learning situations.In the workshops the instrument with 45 values names and descriptions was provided to elicit values. The values could be associated with four different learning scenarios with disruptive technologies. The group interview was held at the workshop at 2,5 h, the groups jointly discussed the values. The participants of the group workshop were heterogeneous: students, educators, technical support personnel at institutes, educational technology developers. There were 4 groups with 4-5 persons in each workshop. The values were collected using the anonymous online survey approach. The workshops were conducted in the national languages and data were translated into English.</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>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'
<div> <h3>Compendium DOI:</h3> <p><a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a> </p> </div> <p>The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <div> <h3>Maintainer of this repository:</h3> </div> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>)</p> <div> <h3>Published paper:</h3> </div> <p>Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. <em>Journal of Paleolithic Archaeology</em> (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a></p> <div> <h3>Abstract:</h3> </div> <p>High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy.</p> <div> <h3>Keywords:</h3> </div> <p>Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy</p> <div> <h3>Overview of contents and how to reproduce:</h3> </div> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <div> <h3>Licenses:</h3> </div> <p>Code: <strong>MIT</strong> <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024).</p> <p>Data and intellectual work: <strong>Creative Commons Attribution 4.0 International License</strong> (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Effect of MgO sintering additive on mullite structures manufactured by fused deposition modeling (FDM) technology
<p>An optimized recipe for 3D printing of Mullite-based structures was used to investigate the effect of MgO sintering additive on the processing stages and final ceramic properties. To achieve dense 3:2 mullite, ceramic filaments were prepared based on an alumina powder, a methyl silicone resin, EVA elastomeric binder and MgO powder. Using 1 wt% MgO and a dwell time of 5 h at 1600 °C, a dense mullite structure could be obtained from filaments with a diameter of 1.75 mm. Ceramic structures with and without sintering additive were printed in vertical and horizontal direction, to investigate the effect of printing direction on mechanical strength after sintering. Using four-point bending test, it was demonstrated that by using MgO, the printing orientation did not affect the mechanical strength significantly anymore. The low Weibull modulus could be explained by the closed porosity that emerge during the degassing of the preceramic polymer due to cross-linking.</p>
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
<p>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></p> <p>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:</p> <ul> <li> <p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p> </li> <li> <p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p> </li> <li> <p>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.</p> </li> <li> <p>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).</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> </li> <li> <p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> <ul> <li> <p>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>].</p> </li> <li> <p>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.</p> </li> <li> <p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p> </li> <li> <p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p> </li> <li> <p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p> </li> <li> <p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p> </li> <li> <p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> <li> <p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> </ul> </li> <li> <p>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%).</p> </li> <li> <p>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.</p> </li> <li> <p>device_Lat—Latitude of the measurement point gathered from the GPS.</p> </li> <li> <p>device_Lon—Longitude of the measurement point gathered from the GPS.</p> </li> </ul> <p>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.</p>
Massive Health Education through Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil
<p><strong>Repository </strong></p> <p><strong>Dataset name: </strong>avasus_syphilis_trail_dataset.csv </p> <p><strong>Version:</strong> 1.0 </p> <p><strong>Dataset period: </strong>05/12/2016 - 01/14/2022 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>177732<strong> </strong></p> <p><strong>Number of Attributes: </strong>16 </p> <p><strong>Missing Values: </strong>Yes </p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c).</p> </li> </ul> <p><strong>Description:</strong> The data contained in the avasus_syphilis_trail_dataset.csv dataset (see Table 1) originate from AVASUS users who have taken a course on the “Syphilis and other STI” learning path. This dataset provides elemental data to analyze the impact and reach of the trails and the profile of their participants.</p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Source</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier of the user (anonymously).</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_gender</strong></p> </td> <td> <p>Gender of the user. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English: Female, Male or Uninformed)</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_occupation</strong></p> </td> <td> <p>User occupation</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_cnes</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the user works.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>CNES Code or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_level_attention</strong></p> </td> <td> <p>Identification of the health care network level for which the user works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.</p> <p>(In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")</p> <p>Or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_region</strong></p> </td> <td> <p>Brazilian region in which the user resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). Other options: “Exterior” or “Não Informado” (In English: Outside or Not informed).</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_id</strong></p> </td> <td> <p>Unique identifier of the course performed by the avasus user.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Code list according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name of the course taken by the avasus user.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>Course name according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_workload</strong></p> </td> <td> <p>Course timetable.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 120.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_creation_date</strong></p> </td> <td> <p>Course creation date.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_id</strong></p> </td> <td> <p>Unique identification of the enrollment carried out by the student in some course of the trail.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated single integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_creation</strong></p> </td> <td> <p>Date the student registered.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_completion_date</strong></p> </td> <td> <p>Date the student completed the course.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_current_progress</strong></p> </td> <td> <p>Student progress regarding course completion.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 100.</p> <br> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_text_evaluation</strong></p> </td> <td> <p>Comment made by the student about the course.</p> </td> <td> <p>Categorial. </p> </td> <td> <p>Free text or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> </tbody> </table> <p><br> <br> <br> </p> <p><strong>References </strong></p> <p>[1] Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114 . </p> <p>[2] Brasil (2022b). Classificação brasileira de ocupações - CBO. Available from: http://www.mtecbo.gov.br/cbosite/pages/home.jsf . </p> <p>[3] Brasil (2022c). Cadastro nacional de estabelecimentos de saúde - CNES. Available from: http://cnes.datasus.gov.br/ .</p> <p><strong>Article: </strong>Massive Health Education with Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil</p>
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
Regional summary statistics for 1107 protein targets based on the Olink technology
<p>This data set contains regional summary statistics (±500kb around the protein coding gene) for a total of 1107 protein - gene combinations as measured by the Olink Proximity Extension Assay in the Fenland study (https://www.mrc-epid.cam.ac.uk/research/studies/fenland/) among 485 individuals. A detailed description of the genetic analysis can be found here https://www.nature.com/articles/s41467-021-27164-0. </p>
2-round Delphi study on digital technologies in vegetable farming in Switzerland
<p>This dataset contains survey data including the codebook for a 2-round Delphi study we conducted in Switzerland in autumn 2020. Selected experts were asked to describe the future of digital technologies in vegetable farming in Switzerland.</p>
Projects and Organizations in Open Sustainable Technology
<p><strong>A curated database of open technology projects and organisations working for a stable climate, energy supply and natural resources. The dataset was created by the <a href="https://opensustain.tech/">Open Sustainable Technology Initiative</a></strong></p>
Story Map of the AI Ethics Lab of the Austrian Institute of Technology (AIT)
<p>The Co-Change Lab at AIT, the Austrian Institute of Technology, focuses on addressing the promises and challenges associated with research work on and the application of machine learning and artificial intelligence. An interdisciplinary team of social and data scientists is working on AI ethics.</p>
Dataset: energy consumption patterns in a science and technology park
<p>This dataset contains time series of energy consumption and external temperature for a group of buildings in a science and technology park, from 2018 to 2022, that is suitable for the development of algorithms to improve energy efficiency and for the early detection of energy consumption peaks based on night-time outdoor temperatures.</p> <p>Time series of power, energy consumption and external temperature for group of tertiary buildings, from 2018-01-01 to 2022-09-15.</p> <p>Peaks in electricity consumption are a major concern for building owners, especially during summer, when external temperatures are high, and users demand air conditioning. Owners may face high costs, observe increased risk of overheating in energy intensive equipment, and may exceed the power threshold set out in the electricity supply contract.</p> <p>Several effective "peak-shaving" strategies can be put in place, such as a higher temperature set-point (which implies a temporary reduction of comfort levels), switching off low-priority processes, and starting the cooling process earlier than usual.</p> <p>This dataset can be used to develop algorithms for early detection of peaks in energy consumption, based on external temperatures measured during the night.</p> <p> </p>
Data for: Generation of sanitation system options for urban planning considering novel technologies
<p>This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information.</p> <p>The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).</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.