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47,155 results for “evaluations”
Downscaled 1 km CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly data from CESM2 simulation HIST-EC over the period 1960-1999, downscaled to the 1 km RACMO grid using elevation class output.</p> <p>Variable 'QICE' represents SMB as calculated internally by CLM.</p>
SNEMI3D dataset pre-processed for evaluation
<p>These files are pre-processed .tif files for the evaluation of my workflow to predict 3D segmentation of neurons from EM image data.</p> <p>The original dataset was published in the context of the IEEE International Symposium on Biomedical Imaging 2013. Biological findings and further information about hte dataset can be found in following article:</p> <p>Kasthuri, N., Hayworth, K. J., Berger, D. R., Schalek, R. L., Conchello, J. A., Knowles-Barley, S., ... & Roberts, M. (2015). Saturated reconstruction of a volume of neocortex. <em>Cell</em>, <em>162</em>(3), 648-661.</p>
Wikidata's linked data for cultural heritage digital resources: an evaluation based on the Europeana Data Model
<p>Wikidata is an open data source with many potential applications. Our study aims to evaluate the usability of Wikidata as a linked data source for acquiring richer descriptions of digital objects within the context of Europeana, a data aggregator from the cultural heritage domain. Specifically, we aim to crawl and convert Wikidata using the standard approaches and operations developed for the (Semantic) Web of Data, i.e. using technologies like linked data consumption and RDF(S)/OWL ontology expression and reasoning. We also seek to re-use existing “semantic” specifications, such as conversions to and from generic data models like Schema.org and SKOS. We have developed an experimental set-up and accompanying software to test the feasibility of this approach. We conclude that Wikidata’s linked data is able to express an interesting level of semantics for cultural heritage, but quality can still be improved and a human operator still must assist linked data applications to interpret Wikidata’s RDF.</p>
GAED: Game Acceptance Evaluation Dataset
<p>The Game Acceptance Evaluation Dataset (GAED) contains statistical data aswell training and validation sets used in our experiments on <em>Neural Networks to evaluate Video Games Acceptance</em>.</p> <p>Please consider citing the following references if you found this dataset useful:</p> <p>[1] Augusto de Castro Vieira, Wladmir Cardoso Brandão. Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews. In Proceedings of the 30th ACM Conference on Hypertext and Social Media. 2019.</p> <p>[2] Augusto de Castro Vieira, Wladmir Cardoso Brandão. GA-Eval: A Neural Network based approach to evaluate Video Games Acceptance. In Proceedings of the 18th Brazilian Symposium on Computer Games and Digital Entertainment. 2019.</p> <p>[3] Augusto de Castro Vieira, Wladmir Cardoso Brandão. (2019). GAED: The Game Acceptance Evaluation Dataset (Version 1.0) [Data set]. Zenodo. </p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
An evaluation of compression algorithms applied to moving object trajectories
<p>This file contains the dataset, source code and results presented in the paper entitled "An evaluation of compression algorithms applied to moving object trajectories" published in the International Journal of Geographical Information Science in 2019.</p> <p>Abstract: The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.</p>
Data and code: Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non‐randomised pilot study
<p>This is a dataset and Stata analytical code relating to prescribing issues identified in the GPP pilot feasibility study. The paper reporting this study has been published as follows: </p> <p>Cardwell K, Smith SM, Clyne B on behalf of the General Practice Pharmacist (GPP) Study Group, et al. Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non-randomised pilot study. BMJ Open 2020;10:e035087. doi: 10.1136/bmjopen-2019-035087</p> <p>The abstract of the study is included below:</p> <p><strong>Objective:</strong> Limited evidence suggests integration of pharmacists into the general practice team could improve medicines management for patients, particularly those with multimorbidity and polypharmacy. This study aimed to develop and assess the feasibility of an intervention involving pharmacists, working within general practices, to optimise prescribing in Ireland.</p> <p><strong>Design:</strong> Non-randomised pilot study</p> <p><strong>Setting:</strong> Primary care in Ireland</p> <p><strong>Participants:</strong> Four general practices, purposively sampled and recruited to reflect a range of practice sizes and demographic profiles.</p> <p><strong>Intervention:</strong> A pharmacist joined the practice team for six months (10 hours/week) and undertook medication reviews (face-to-face or chart-based) for adult patients, provided prescribing advice, supported clinical audits, and facilitated practice-based education.</p> <p><strong>Outcome measures:</strong> Anonymised practice-level medication (e.g. medication changes) and cost data were collected. Patient-Reported Outcome Measure (PROM) data were collected on a subset of older adults (aged ≥65 years) with polypharmacy using patient questionnaires, before and six weeks after medication review by the pharmacist.</p> <p><strong>Results:</strong> Across four practices, 787 patients were identified as having 1,521 prescribing issues by the pharmacists. Issues relating to potentially inappropriate or high-risk prescribing were addressed most often by the prescriber (51.8%), compared to cost-related issues (7.5%). Medication changes made during the study equated to approximately €57,000 in cost savings assuming they persisted for 12 months. Ninety-six patients aged ≥65 years with polypharmacy were recruited from the four practices for PROM data collection and 64 (66.7%) were followed up. There were no changes in patients’ treatment burden or attitudes to deprescribing following medication review, and there were conflicting changes in patients' self-reported quality of life.</p> <p><strong>Conclusions:</strong> This non-randomised pilot study demonstrated that an intervention involving pharmacists, working within general practices is feasible to implement and has potential to improve prescribing quality. This study provides rationale to conduct a randomised controlled trial to evaluate the clinical and cost-effectiveness of this intervention.</p>
2nd cycle evaluation experiment ULM sim AutoMate
<p>This experiment has been conducted at the driving simulator to test the first enabler integrated version of the simulator 1 demonstrator. The data of 26 participants was collected where each participant drove through both, the baseline and the TeamMate condition in the Peter scenario. There was a special focus of the gaze behavior, therefore the included dataset involves the gaze behavior as well as the subjective rating. The description of the experiment and the results can be found in the Deliverable D6.2 “Results of Evaluation in the 2nd cycle”.</p>
3rd cycle evaluation experiment ULM sim AutoMate
<p>This experiment has been conducted at driving simulator 1 to test the final integrated version of the simulator demonstrator in the Peter scenario with all integrated and updated enablers in their final state. 18 users participated in the final experiment. The dataset consists of two files, one the zip file of the simulator logs (see Table 3) and the other file is the subjective rating of both systems. The description of the experiment and the results can be found in the Deliverable D6.3 “Results of Comparative Evaluation after 3rd cycle”.</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
Analytical Protocols for Quantitative Evaluation of [4Fe-4S]-maquette Spectra
<p>A step-by-step tutorial is presented for the quantitative analysis of UV-vis spectra of <em>'as isolated'</em> [4Fe-4S]-maquettes that contain diamagnetic, [4Fe-4S]<sup>2+</sup> clusters, as chromophores. The UV-vis model is justified by the schematic molecular orbital diagram of Fe-S(sulfide) and Fe-S(thiolate) bonding. The spectral analysis is based on integrated peak intensities, which is superior to using absorption maxima (<span class="math-tex">\(\lambda _{max}\)</span>), when the chemical composition of the chromophores are not identical, but similar. Three spectra (two model cluster complexes and a metalloprotein active site) provide the reference for converting spectral intensities to concentrations.</p> <p>The UV-vis modeling is demonstrated for a prototypical <span class="math-tex">\([Fe_4S_4(CIACGAC)(\beta ME)]^{2-}\)</span> complex (FdM-7 maquette) and the <span class="math-tex">\([Fe_4S_4(\beta ME)_4]^{2-}\)</span> complex formed under identical conditions, but without the presence of a peptide.</p>
Trace-Share Dataset for Evaluation of Statistical Characteristics Preservation
<p>The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "<strong>trace-share</strong>" to avoid false detection by antivirus software.</p> <p>For more information, see the project repository at <strong><a href="https://github.com/Trace-Share">https://github.com/Trace-Share</a></strong>.</p> <p> </p> <p><strong>Selected Attack Traces</strong></p> <p>We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.</p> <p> </p> <p><strong>Background Traffic Data</strong></p> <p>Publicly available dataset <a href="https://www.unb.ca/cic/datasets/ids-2018.html">CSE-CIC-IDS-2018</a> was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.</p> <p> </p> <p><strong>Evaluation Results and Dataset Structure</strong></p> <ul> <li>Traces variants (<a href="https://zenodo.org/record/3553063/files/traces-normalized.zip">traces-normalized.zip</a>, <a href="https://zenodo.org/record/3553063/files/traces-adjusted.zip">traces-adjusted.zip</a>) <ul> <li>./traces-normalized/ — normalized PCAP files and details in YAML format;</li> <li>./traces-adjusted/ — configuration files for traces combination in YAML format.</li> </ul> </li> <li>Computed statistics (<a href="https://zenodo.org/record/3553063/files/statistics.zip">statistics.zip</a>) <ul> <li>./statistics-background/ — background traffic statistics computed by ID2T;</li> <li>./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);</li> <li>./statistics-difference/ — computed mean and median differences of background and combined traffic traces.</li> </ul> </li> <li>Evaluation results <ul> <li><a href="https://zenodo.org/record/3553063/files/statistics-difference.ipynb">statistics-difference.ipynb</a> — file containing visualization of statistics differences.</li> </ul> </li> </ul>
A Sampling Approach to Evaluative Conditioning
<p>In an evaluative conditioning procedure (EC), a conditioned stimulus (CS) is paired with positive or negative unconditioned stimuli (US) and consequently acquires US valence. We introduce a sampling perspective into the EC paradigm by repeatedly giving participants agency over which CSs they want to sample (i.e., for what CSs they want to see conditioning trials). Half of the CSs was paired positively, while the other half was paired negatively. We also introduced a yoked condition, in which participants saw the same information produced by the sampling condition, but did not get to make any decisions. In three experiments, we found that over time a preference for sampling positively paired CSs over negatively paired CSs develops. We replicated standard EC effects and found that a higher number of pairings leads to more positive evaluative shifts, but more so in the sampling than in the yoked condition. This finding shows that evaluative conditioning depends on the learner’s willingness to attend to certain CSs. The results of this set of studies also suggest that agency increases the readiness to process the presented CS-US pairs. This reasoning is supported by memory data that indicate better memory for the number of pairings and paired valence in the sampling condition compared to the yoked condition. Implications for the external validity of the EC paradigm are also discussed.</p>
Evaluation data used in "An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists"
<p>This repository contains all data of the evaluation questionnaire used to assess modifications in pupils’ perceptions of same water resources concepts and science and scientist resulting from the application of OH-Kids outreach model in six Mexican primary schools (n=344 pupils).</p>
Industry-scale Application and Evaluation of Deep Learning for Drug Target Prediction
<p>Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.</p>
Figure 3 in Intragonadal evaluation of sexual steroid hormones during three reproductive events in two species of Peromyscus (Rodentia: Cricetidae)
Figure 3. Fluctuations of each intraovarian [SSH] in the ∆4 pathway throughout three reproductive events in two species of Peromyscus. Mean concentrations of sexual steroid hormones, [SSH], were obtained from estrous cycle, pregnancy and lactation in free-living, adult females of P. melanotis (A) and P. difficilis (B). Symbology as in Fig. 2. Note that scales differ; complete ANOVA information is available in Table S2.
Figure 1 in Intragonadal evaluation of sexual steroid hormones during three reproductive events in two species of Peromyscus (Rodentia: Cricetidae)
Figure 1. Intraovarian contents of selected ∆ 4 pathway's SSH in two Peromyscus species. Sexual steroid hormones (SSH: progesterone, P4; androstenedione, A; testosterone, T; estradiol, E2) were obtained from free-living, adult females of P. melanotis (A) and P. difficilis (B), during a complete estrous cycle (CEC: proestrus to diestrus), and after ovulation (vertical arrows) followed by fecundation in a successful estrous cycle (SEC: proestrus, estrus + early gestation 1 and late gestation 2 + overall lactation); note that proestrus and estrus data from CEC are duplicated in SEC). The oogenetic and anabolic/ catabolic phases of the ovarian cycle are also depicted (see Table 1).
Figures 3-4 in Blood metabolites as predictors to evaluate the body condition of Neopelma pallescens (Passeriformes: Pipridae) in northeastern Brazil
Figures 3-4. Spearman's correlation between Body Condition Index (BCI) and glucose concentration for breeding (3) (N = 28) and non-breeding (4) (N = 46) individuals of N. pallescens in Reserva Biológica de Guaribas, PB. The breeding period of N. pallescens started at the end of July (P1) and climaxed in January (P3). It occurred throughout Figure 2. Monthly samples sizes of captured individuals of N. palles- the rainy and dry seasons. Glucose concentrations showed cens in Reserva Biológica de Guaribas, PB. Recaptured individuals significant variation during the developmental phases of brood are not included. patches with a higher concentration in P3 (Fig. 5) (ANOVA, F = 5.39, p = 0.01). Tukey's test showed significant values only The glucose concentration was negatively correlated with between P1 and P3 (Tukey's Test, P2-P1: p = 0.76, P3-P1 = 0.01, body condition (Table 1). Further analyses showed that the glu- P3-P2: p = 0.05).
Fig. 1 in Evaluation of Intracellular Ca Concentration by Fura 2 Ratiometry in Encystment-induced Colpoda cucullus
Fig. 1. The elevation in the intracellular Ca2+ concentration (F /F 340 380 ratio) in encystment-induced C. cucullus. F340 and F380 are the fluorescence intensities of fura 2 excited at 340-nm and 380-nm light, respectively. The cells loaded with fura 2-AM were suspended in 1 mM Tris-HCl buffer (pH 7.2) containing 0.1 mM CaCl2 (open circles), the same buffer without addition of CaCl2 (closed squares or closed circles), and the same buffer containing 10 µM EGTA (open squares) at high (50,000 cells/ml) ('H') or low (2,000 cells/ ml) ('L') cell density. Open triangles show the F340/F380 ratios of the cells suspended in 1 mM Tris-HCl buffer (pH 7.2) without addition of 0.1 mM CaCl2 at a low cell density (2,000 cells/ml) together with a high-density (48,000 particles/ml) PLP (26 µm in diameter, Sigma-Aldrich) ('L/PLP'). Points and attached bars correspond to the means of 5 identical measurements and standard errors.
Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales. in Early Cretaceous Monocots: A Phylogenetic Evaluation
Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales.
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.