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10,554 results for “measurements”

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zenodo36/100

Synthesis of results in Digital Competence in before-after measures

<p>Synthesis of results in Digital Competence in before-after measures</p>

opencc-by-4.0May 2022View details →
zenodo36/100

Data release for the paper "Measurements of protons and charged pions emitted from the $\nu_{\mu}$ charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector"

<p>This data release is associated with the paper &quot;Measurements of protons and charged pions emitted from the <span class="math-tex">\(\nu_{\mu}\)</span>&nbsp;charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector&quot;. It is currently available on&nbsp;<a href="http://arxiv.org/abs/2203.08367">arXiv:2203.08367</a>&nbsp;and to be submitted to Phys. Rev. D.</p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p>The provided zip file contains the data as below.</p> <ol> <li>event.root: Event by event information of 183 iron-target interactions.</li> <li>plot.root: Plot information as shown in the paper.</li> <li>detector_efficiency.root: Detectrion efficiencies for muons, charged pions, and protons.</li> <li>momentum_resolution.root: Relation between true and reconstructed momentum for muons, charged pions, and protons.</li> <li>misPID.root: Mis-PID rates of protons and pions.</li> <li>syscov.root: Covariance matrices of systematic uncertainties.</li> <li>flux.root: The neutrino flux and the covariance matrix of the flux error.</li> </ol> <p>The zip file&nbsp;also contains a README.pdf file with detailed information on the included files. Please read it.</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

Joint acoustical-electrical modeling for tight sandstones verified by measurements

<p>These data are the compressional-wave waveform and electrical data obtained by Ba et al. through ultrasonic experimental &nbsp;and conductivity measurements on tight sandstones at different confining pressures and fluid saturations.</p> <p>Details are given in the uploaded introduction document regarding the format of dataset.</p>

opencc-by-4.0Jun 2022View details →
zenodo36/100

Development of a Multi-Level Dynamic Model to Measure the Resilience Level of Transportation Infrastructure Networks

<p>The recent increase in disasters is making the largest critical infrastructure system namely the transportation infrastructure system susceptible to unexpected damage. Discontinuation of services provided by transportation infrastructures will create significant societal, economic, and collateral damages. Therefore, this study aims to identify dimensions to measure the resilience of the transportation infrastructures. This study also aims to develop a model to measure the resilience of the transportation infrastructures resilience. To fulfill the aims of this study, a questionnaire was developed which was supported by a comprehensive literature review. 92 valid responses were received and analyzed qualitatively and quantitatively. Statistically significant variables were used to develop a resilience measurement tool. The developed tool will provide relative resilience measures for multiple projects which will help in identifying the most vulnerable segment of the transportation infrastructure network. Exploratory factor analysis (EFA) was performed to identify the constructs and structural equation modeling (SEM) was used to develop the model. Without previous experience in reconstruction works, handling integrated assets becomes very critical. Also, such inexperience makes it difficult to handle emergency resources properly. However, such issues regarding integrated assets can be resolved by investing in locating integrated assets away from the roadways, so if a break in a railroad crossing or utility line occurs or emergency repairs are needed, the impact on the roadway operations can be minimized. To avoid issues related to access to previous disaster data for the roadway this study suggess investing in preparing an interactive online platform for recording and reviewing data related to disasters as well as previous resilience enhancing activities for the roadway with easy access credentials. The findings of this study will support practitioners and decision-makers in investing in the appropriate resilience enhancement activity project for funding and investment.</p>

opencc-by-4.0Jul 2021View details →
zenodo36/100

Soil ecotoxicology needs robust biomarkers – a meta-analysis approach to test the robustness of gene expression-based biomarkers for measuring chemical exposure effects in soil invertebrates

<p>Gene expression-based biomarkers are&nbsp;regularly proposed&nbsp; as rapid, sensitive and mechanistically informative tools to identify whether&nbsp;soil&nbsp;invertebrates are experiencing adverse effects due&nbsp;to&nbsp;chemical exposure. However, before biomarkers could be deployed within diagnostic studies, systematic evidence of the robustness of such biomarkers to detect effects&nbsp; is needed.&nbsp;Here, we present an approach for conducting a systematic meta-analysis of the robustness of gene expression-based biomarkers in soil invertebrates.</p> <p>The approach was developed and trialled for two measurements of gene expression commonly proposed as biomarkers in soil ecotoxicology: metallothionein (MT) gene expression in earthworms for metals and heat shock protein 70 (HSP70) gene expression in earthworms for organic chemicals. From a systematic analysis of the published literature, we collected 294 unique gene expression data points and used linear mixed-effect models to&nbsp;assess concentration,&nbsp;exposure duration&nbsp;and species effects on the quantified response.</p> <p>This database provided contains gene-expression data from publications that have used gene expression-based biomakers to study effects of chemical pollutants on soil invertebrates. R scripts are provided that were used to study the patterns of gene expression as reported in accompanying publication.&nbsp;</p> <p>We encourage colleagues in the field to apply this approach to other biomarkers, as such quantitative assessment is a prerequisite to ensuring that the suitability and limitations of proposed biomarkers are known and stated.</p>

opencc-by-4.0Jul 2022View details →
dryad36/100

Morphological measurements of two host specialists of the dipteran Tephritis conura, both in sympatry and allopatry

<div> <div> <div> <div> <p>Adaptation to new ecological niches is known to spur population diversification and may lead to speciation if gene flow is ceased. While adaptation to the same ecological niche is expected to be parallel, it is more difficult to predict whether selection against maladaptive hybridization in secondary sympatry results in parallel divergence also in traits that are not directly related to the ecological niches. Such parallelisms in response to selection for reproductive isolation can be identified through estimating parallelism in reproductive character displacement across different zones of secondary contact. Here, we use a host shift in the phytophagous peacock fly Tephritis conura, with both host races represented in two geographically separate areas East and West of the Baltic Sea to investigate convergence in morphological adaptations. We asked i) if there are consistent morphological adaptations to a host plant shift and ii) if the response to secondary sympatry with the alternate host race is parallel across contact zones. We found surprisingly low and variable, albeit significant, divergence between host races. Only one trait, the length of the female ovipositor, which serves an important function in the interaction with the hosts, was consistently different between host races. Instead, co-existence with the other host race significantly affected the degree of morphological divergence, but the divergence was largely driven by different traits in different contact zones. Thus, local stochastic fixation or reinforcement could generate trait divergence, and additional evidence is needed to conclude whether divergence is locally adaptive.</p> </div> </div> </div> </div>

opencc-zeroJun 2022View details →
zenodo36/100

An analytical framework to measure social return of community-supported agriculture

<p>Figures of the case studies (original data from CSA websites and staff interviews)</p>

opencc-by-4.0Feb 2022View details →
dryad36/100

FishShapes v1: functionally relevant measurements of teleost shape and size on three dimensions

<p>Teleost fishes account for 96% of all fish species and exhibit a spectacular variety of body forms. Teleost lineages range from deep-bodied to elongate (e.g. eels, needlefish), laterally compressed (e.g. ribbonfish) to globular (e.g. pufferfish) and include uniquely shaped lineages such as seahorses, flatfishes and ocean sunfishes. Adaptive body shape convergence within fishes has long been hypothesized but the nature of the relationships between fish form and ecological and environmental variables remain largely unknown at the macroevolutionary scale. To facilitate the investigation of the interacting factors influencing teleost body shape evolution we measured 8 functionally relevant linear traits on adult-sized specimens along with specimen mass. Linear measurements of standard length, maximum body depth, maximum fish width, lower jaw length, mouth width, head depth, minimum caudal peduncle depth and minimum caudal peduncle width were taken in millimeters with calipers, or tape measures for oversized specimens. We measured these traits on a total of 16523 specimens (1-3 specimens per species) at the Smithsonian National Museum of Natural History and took approximately 7000 person hours of data collection to complete. The data went through a three-step error-checking process to clean and validate the data and then species averages were calculated. We present the complete specimen dataset, which encompasses approximately one fifth of extant teleost species diversity, spanning ~90% of teleost families and ~96% of orders. The species and family names are compatible with the FishBase taxonomy (Pauly &amp; Froese, 2019) and the order information with the phylogenetically informed taxonomy of Betancur-R et al. (2014). This dataset is licensed under a Creative Commons Attribution - Non-Commercial 4.0 International License (CC BY-NC), please cite this paper when using the data or a subset of it.</p>

opencc-zeroJun 2022View details →
dryad36/100

Increase in coercive measures in psychiatric hospitals during the COVID-19 Pandemic

<p class="MsoNormal"><strong><span>Objective</span></strong><span>: To examine whether the pandemic in 2020 caused changes in psychiatric hospital cases, the percentage of patients exposed to coercive interventions, and aggressive incidents. </span></p> <p class="MsoNormal"><span><strong>Results: </strong>The number of cases in adult psychiatry decreased by 7.6% from 105,782 to 97,761. The percentage of involuntary cases increased from 12.3 to 14.1%, and the absolute number of coercive measures increased by 4.7% from 26,269 to 27,514. The percentage of cases exposed to any kind of coercive measure increased by 24.6% from 6.5 to 8.1%, and the median cumulative duration per affected case increased by 13.1% from 12.2 to 13.8 hrs, where seclusion increased more than mechanical restraint. The percentage of patients with aggressive incidents, collected in 10 hospitals, remained unchanged. </span></p> <p class="MsoNormal"><span><strong>Conclusions: </strong>While voluntary cases decreased considerably during the pandemic, involuntary cases increased slightly. However, the increased percentage of patients exposed to coercion is not only due to a decreased percentage of voluntary patients, as the duration of coercive measures per case also increased. The changes that indicate deterioration in treatment quality were probably caused by the multitude of measures to manage the pandemic. The focus of attention and internal rules as well have shifted from prevention of coercion to prevention of infection.</span></p>

opencc-zeroJun 2022View details →
zenodo36/100

Leaf thickness measurements of plants

<p># Analysis Scripts for &#39;Leveraging Plant Dynamics Using Physical Reservoir Computing&#39;</p> <p>There is leaf thickness and physiological data available from three experiments: a control experiment and two strawberry experiments. In each experiment, the environmental conditions of a growth chamber are modulated (light intensity, &nbsp;temperature and relative humidity) and a single strawberry plant is located inside the chamber. Leaf thickness measurement clips are mounted on the plant except for the control experiment. In this case a plant is still inserted but the clips are not mounted. Physiological data of the plant is collected in all three experiments using a LI6400XT photosynthesis system.</p> <p>An overview of the available parameters is included below. N/A refers to a sensor that is not calibrated and/or temperature compensated. Calibration data is available TODO</p> <p>| parameter &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | unit &nbsp; &nbsp; &nbsp;| description &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> |-----------------------------------------------------|-----------|----------------------------------------------------|<br> | light_sensor_lux &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| lux &nbsp; &nbsp; &nbsp; | light intensity (humain) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_1_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_1_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_1_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_2_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_2_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_2_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_3_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 3 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_3_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 3 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_3_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 3 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_4_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_4_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_4_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_5_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_5_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_5_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_6_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_6_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_6_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_7_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 7 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_7_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | raw leaf thickness measurement &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_7_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 7 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_8_um &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | um &nbsp; &nbsp; &nbsp; &nbsp;| thickness of leaf clip 8 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_thickness_8_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | thickness of leaf clip 8 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | leaf_temp_8_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | temperature of leaf clip 8 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | ref_mon_0 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | monitor of the 3.3V ADC reference (board 0) &nbsp; &nbsp; &nbsp; &nbsp;|<br> | ref_mon_1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | monitor of the 3.3V ADC reference (board 1) &nbsp; &nbsp; &nbsp; &nbsp;|<br> | ref_mon_2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | monitor of the 3.3V ADC reference (board 2) &nbsp; &nbsp; &nbsp; &nbsp;|<br> | ref_mon_3 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | N/A &nbsp; &nbsp; &nbsp; | monitor of the 3.3V ADC reference (board 3) &nbsp; &nbsp; &nbsp; &nbsp;|<br> | soil_water_content_nc_au &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| N/A &nbsp; &nbsp; &nbsp; | soil water concentration &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | air_temperature_C &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | degree C &nbsp;| air temperature &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | relative_humidity_percent &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | % &nbsp; &nbsp; &nbsp; &nbsp; | relative humidity &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_external_probe_air_temperature_C &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | degree C &nbsp;| air temperature of external probe &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_external_probe_relative_humidity_percent &nbsp; | % &nbsp; &nbsp; &nbsp; &nbsp; | rel. humidity of external probe &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_photosynthetic_rate_umol/m2/s &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| umol/m2/s | photosynthetic rate &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_stomatal_conductance_mol/m2/s &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| mol/m2/s &nbsp;| stomatal conductance &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_transpiration_rate_mmol/m2/s &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | mmol/m2/s | transoration rate &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_vapour_pressure_deficit_kPa &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| kPa &nbsp; &nbsp; &nbsp; | vapour pressure deficit &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_sample_cell_air_temperature_C &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| degree C &nbsp;| air temperature in the sample cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_leaf_temperature_C &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | degree C &nbsp;| temperature of leaf inside sample cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_ref_cell_CO2_conc_umol/mol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | umol/mol &nbsp;| CO2 concentration in the reference cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_sample_cell_CO2_conc_umol/mol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| umol/mol &nbsp;| CO2 concentration in the sample cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_ref_cell_H2O_conc_mmol/mol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | mmol/mol &nbsp;| H2O concentration in the reference cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_sample_cell_H2O_conc_mmol/mol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| mmol/mol &nbsp;| H2O concentration in the sample cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_ref_cell_relative_humidity_conc_percent &nbsp; &nbsp;| % &nbsp; &nbsp; &nbsp; &nbsp; | Rel. humidity in the reference cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_sample_cell_relative_humidity_conc_percent | % &nbsp; &nbsp; &nbsp; &nbsp; | Rel. humidity in the sample cell &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_PAR_inside_chamber_umol/m2/s &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | umol/m2/s | PAR inside leaf chamber &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | li6400xt_PAR_outside_chamber_umol/m2/s &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| umol/m2/s | PAR outside leaf chamber &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | li6400xt_air_pressure_kPa &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | kPa &nbsp; &nbsp; &nbsp; | air pressure &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | time &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| time &nbsp; &nbsp; &nbsp;| sample time &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> | train_val_test_split &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | data split on da day-basis, all days equal &nbsp; &nbsp; &nbsp; &nbsp; |<br> | train_val_test_split2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | data split on da day-basis, train focus on first 5 |<br> | di_2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-2h from center of night &nbsp; &nbsp; |<br> | di_4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-4h from center of night &nbsp; &nbsp; |<br> | di_6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-6h from center of night &nbsp; &nbsp; |<br> | di_9 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-9h from center of night &nbsp; &nbsp; |<br> | di2_2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-2h from center of night &nbsp; &nbsp; |<br> | di2_4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-4h from center of night &nbsp; &nbsp; |<br> | di2_6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-6h from center of night &nbsp; &nbsp; |<br> | di2_9 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | discard interval for +-9h from center of night &nbsp; &nbsp; |</p> <p>Non-calibrated variables are usually RAW ADC readout values. The ADC range is 0-3.3V, where the midpoint is at 1.65V (0x0).</p> <p>`train_val_test_split` interleaves the train and test splits, such that drift in the system is automatically compensated for, while `train_val_test_split2` does not. The test data is always at the end of the analysis. `train_val_test_split` should be used with `di_`, and `train_val_test_split2` should be used with `di2_`.</p> <p>Three data formats are available: `data`, `full_data` and `mini_data`. `data` was used to generate the results. It is a cropped version of `full_data` that discards part of the start of the experiment and end to remove transient effects at the start. `mini_data` is a subsampled dataset, with sample spacing of 60s (sample interval), which is useful for plotting and fast analysis.</p>

opencc-by-4.0Jan 2021View details →
zenodo36/100

Frequency dataset for "Profile-based measures of lexical variation. Four case studies on variation in word choice between Belgian and Netherlandic Dutch."

<p>The dataset is structured according to:</p> <ul> <li>the lexical field (CLOTHING, TRAFFIC, IT, and EMOTION);</li> <li>the part of speech (noun or adjective);</li> <li>the corpus;</li> <li>the concept;</li> <li>the term.</li> </ul> <p>It first gives the absolute frequency as found in the corpus and also after it was disambiguated. The concept frequency and relative frequency is calculated based on the &quot;disambiguated&quot; absolute frequency.</p> <p>More information can be found in this&nbsp;dissertation:</p> <p>Daems, Jocelyne. 2022.&nbsp;<em>Profile-based measures of lexical variation. Four case studies on variation in word choice between Belgian and Netherlandic Dutch. </em>KU Leuven.</p>

opencc-by-4.0Jun 2022View details →
zenodo36/100

Optimization and evaluation of a two-stage chromogenic tenase assay for measurement of emicizumab plasma levels

<p>A two-stage chromogenic tenase assay allows specific measurement of emicizumab plasma levels over a broad concentration range (10 &micro;g/ml to 100 &micro;g/ml). The assay showed a reasonable agreement with one-stage clotting assy used for emicizumab quantificaition and can be applied on an automated coagulation analyzer, demonstrating its applicability within a routine laboratory setting.</p>

opencc-by-4.0Jun 2022View details →
zenodo36/100

KPIs to measure the impact of Food loss and Waste prevention strategies

<p>These data correspond to&nbsp;the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.</p>

opencc-by-4.0Jun 2022View details →
zenodo36/100

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

<p>We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of &nbsp;10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (&plusmn;10\%), a broadband resolution of 0.000003 gRMS, a measurement range of &plusmn;5 gpk and a frequency range of 0.06 to 450 Hz (&plusmn;5\%) were installed.&nbsp; The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs&nbsp;= 600 Hz&nbsp;using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK).&nbsp;</p>

opencc-by-4.0Jun 2022View details →
zenodo36/100

Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris

<p>Supplementary dataset of the publication:&nbsp; Pliemon, T., Foelsche, U., Rohr, C., and Pfister, C.: Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris, Clim. Past, 2022</p> <p>For more details see the file.</p> <p>&nbsp;</p>

opencc-by-4.0May 2022View details →
dryad36/100

Data from: Integrating 3D models with morphometric measurements to improve volumetric estimates in marine mammals

<p>1. Studies of body condition are key to understanding the health, bioenergetics, and ecological roles of marine mammals. Due to challenges in studying marine mammals at sea, body condition is often approximated using metrics representing the size of the dorsal surface visible from aerial imagery, but quantifying variability in body volume would enable a more holistic understanding of bioenergetics. Further, the number and location of measurements needed to accurately quantify body condition has received little attention. Three-dimensional (3D) models provide a promising tool for representing morphology and providing holistic estimates of marine mammal body condition when combined with field-based morphometric measurements.</p> <p>2. We use humpback whales (Megaptera novaeangliae) to demonstrate the utility of 3D models for estimating body condition in marine mammals. We integrate morphometric measurements taken from Unoccupied Aerial Vehicles (UAVs) with scalable 3D models to generate estimates of humpback whale body volume. We assess which and how many morphometric measurements are required to accurately estimate body volume and compare the error between volume estimates derived from 3D models and previously developed models representing volume as a series of ellipses. Using UAV measurements, we assess the contribution of each morphometric measurement to volumetric estimates, and quantify the error produced by all combinations and numbers of morphometric measurements (131,072 combinations).</p> <p>3. Error in volume estimates from 3D models generated with as few as five width measurements was &lt;5% compared to the full models and was lower than the error produced when using five width measurements with the elliptical approach. We suggest that by conserving the external morphology of marine mammals, 3D models allow body volume and body condition to be estimated accurately with few measurements.</p> <p>4. We provide code and guidelines for creating 3D models using the open-source software Blender and for assessing which measurements are needed to accurately capture the morphology of cetaceans. The 3D modeling approach we present will facilitate studies of intra- and interannual changes in body volume in marine mammals, which is vital to providing a more holistic understanding of bioenergetics and to assessing responses to environmental change and anthropogenic stressors.</p>

opencc-zeroJul 2022View details →
dryad36/100

Switchgrass flowering time measurements for genomic prediction

<p>The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late-flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late-flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1,532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype-by-sequencing (530,792 SNPs). Predictive ability of single-trait and multi-trait models were evaluated by cross-validation, by prediction of a progeny trial (n=122), and through prediction of yield performance in a parallel experiment (n=52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi-trait models provided an advantage during cross-validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per-cycle progress than previously reported field evaluations (3.1 vs 5.0 day<sup>-1</sup> cycle<sup>-1</sup>). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54-0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. These results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low-cost flowering time evaluations.</p>

opencc-zeroJul 2022View details →
dryad36/100

Data from: Protecting great apes from disease: compliance with measures to reduce anthroponotic disease transmission

<p><span>Based on an international sample of past (N=420) and potential future visitors (N=569) to wild great ape tourism sites in Africa, we used an online questionnaire to characterise visitors' practices, assess expectations (e.g., about proximity to great apes) and identify key factors related to potential compliance with disease mitigation measures. This was implemented adapting a framework from health literature (the Health Belief Model; HBM), particularly focused on reducing COVID-19 transmission at an early stage of the pandemic.</span></p>

opencc-zeroJul 2022View details →
zenodo36/100

Temperature measurements of an internal fumarolized area of Vesuvius

<pre>Periodic temperature measurements of an internal fumarolized area of Vesuvius (Italy) carried out starting from 2003 with a Gemini Tinytag plus 2 datalogger TGP4020 range -40+125&deg;C with resolution 0.02&deg;C. </pre> <pre>Measurements are made at a depth of 0.1 m. </pre> <pre>The measurements are taken every 30 minutes for the years 2003-2006 and every hour for the following years.</pre> <pre>The coordinates of the measuring point are 33T 451537.2m E, 4519098.5 m N.</pre> <pre>Due to great difficulties in accessing the measurement site, unfortunately, long periods of non-measurement are present in the record. </pre> <pre>Since the measurement area was affected by a landslide, the data before and after 2007 appear to be slightly different.</pre> <p>&nbsp;</p>

opencc-by-4.0Jul 2022View details →
zenodo36/100

Dataset for "A randomized benchmarking suite for mid-circuit measurements"

<p>Dataset and simulation code to generate the results of&nbsp;&quot;A randomized benchmarking suite for mid-circuit measurements&quot;.</p>

opencc-by-4.0Jul 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record