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Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density
<p>Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "<em>Measuring the local Dark Matter density in the laboratory</em>" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>).</p> <p>Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood. </p>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Monthly values of absolutely measured geomagnetic components (D, I, H, Z, F) of Coimbra observatory in the period1866-2015
<p>Monthly values of the absolutely measured geomagnetic components D, I, H, Z, F for the period 1866-2015 of Coimbra observatory. Different combinations of three magnetic elements were used to fully specify the geomagnetic field vector: the HDI components were measured in 1866-1951; HDZ in 1951-2006; and DIF in 2006-2015. It should also be noted that the monthly values of the first period (1866-1951) are based exclusively on the averages of absolute measurements (made on average 3 per month), while from 1951 onwards the monthly averages were calculated from the hourly averages obtained from the magnetograms after baseline calibration.</p>
Values and attitudes associated with public support for biodiversity and ecosystem services as goals of ecological restoration
<p>Source data and metadata for "Public support for restoration: Does including ecosystem services as a goal engage a different set of values and attitudes than biodiversity protection alone?", accepted to PLOS One in December 2020. </p>
Beyond cost reduction: Improving the value of energy storage.
<p>The here provided ".nc" files are data files from the paper "Beyond cost reduction: Improving the value of energy storage". The data files represent 3 scenarios from a European energy system model PyPSA-Eur. It can be used as input to the Jupyter notebook analysis and plotting scripts provided in <a href="https://github.com/pz-max/Beyond-cost-reduction-Improving-the-value-of-energy-storage">GitHub.</a></p>
Using Corpus Studies to Find the Origins of the Madrigal: Music and Feature Values
<p>This distribution includes files associated with the experiments described in the "Using Corpus Studies to Find the Origins of the Madrigal" paper presented at the 2021 Future Directions of Music Cognition conference (http://org.osu.edu/mascats/). Our group encoded all the music from the original sources ourselves, using Sibelius, and exported the Sibelius data as PDF and MIDI files. The details of the corpus are described in the "Florence 164 Metadata.xlsx" file and in the conference paper itself.</p> <p>All files included in this archive are distributed under a "CC BY-SA 4.0" license" license (https://creativecommons.org/licenses/by-sa/4.0/). </p> <p>All features were extracted using jSymbolic 2.2 (http://jmir.sourceforge.net) directly from the MIDI encodings included here. Details on all the individual features extracted with the software are available in the jSymbolic manual (http://jmir.sourceforge.net/manuals/jSymbolic_manual/home.html). The features are presented as follows in the "F164_Extracted_Features" folder:</p> <p>- F164_Feature_Definitions.xml: Descriptions of all extracted features, encoded in ACE XML 1.0, as output directly by jSymbolic. This file does not itself include any feature values.</p> <p>- F164_Feature_Values.xml: Extracted feature values, encoded in ACE XML 1.0, as output directly by jSymbolic. The features are described in the FeatureDefinitions.xml file. Class associations are implied by the folder containing each MIDI file.</p> <p>- F164_Feature_Values_BasicCSV: Extracted feature values encoded in a CSV file, as output directly by jSymbolic. Class associations are implied by the folder containing each MIDI file.</p> <p>- F164_Feature_Values_HumanReadable.xlsx: Extracted features formatted into a human-readable Microsoft Excel file. Group averages and standard deviations have been added to the bottom.</p> <p>- F164_Feature_Values_WekaReady.csv: Extracted feature values encoded in a CSV file in a format readable by Weka (https://www.cs.waikato.ac.nz/ml/weka/). Class values have been added in a column on the right, and file paths have been removed, as required by Weka.<br> </p>
set of 100000 TERO counter values
<p>2 files (30_30_D.bin and 62_62_D.bin) = 2 examples of stream of random counter values generated by a TERO TRNG, manufactured in a 28 nanometer STM technology. Used in above mentionned paper in fig 3 and 6 left (for 62_62_D.bin) and fig 3 and 6 right (for 30_30_D.bin)</p>
abarbour/pbe-data: PBO BSM data, peak values, and regression biases -- updated with revised data and CRUST1.0 terms
<p>Two new tables are included here, each containing site and source classification terms from the CRUST1.0 global velocity model (see http://igppweb.ucsd.edu/~gabi/crust1.html)</p> <p>Dataset from Barbour and Crowell (2017), Dynamic Strains for Earthquake Source Characterization, <em>Seismol. Res. Lett.</em>, <strong>88</strong> (2A) 354-370; DOI: 10.1785/0220160155</p>
WikiRate metric values dataset
<p>WikiRate is a collaborative platform that enables everyone to contribute publicly available environmental, social and governance (ESG) data. This dataset contains all metric values that have been contributed to the platform.</p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts - auxiliary values
<p>The table contains the population and the size of the total area for each German district as of 2013. Furthermore it contains the size of those areas per district, that potentially could be used for wind energy.</p> <p>The data on the population is provided by the Federal Statistical Office and the statistical Offices of the Länder: © Federal Statistical Office and the statistical Offices of the Länder, Regionaldatenbank Deutschland, December 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (data was changed). The total district area is derived from geo data provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>For further information on potential areas see VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 105-109.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Tabelle umfasst die Bevölkerungsanzahl und Flächengröße je deutschem Landkreis für das Jahr 2013. Außerdem ist die Größe jener Fläche angegeben, die potentiell für die Windenergie genutzt werden könnte.</p> <p>Die Bevölkerungszahlen werden von den Statistischen Ämtern des Bundes und der Länder zur Verfügung gestellt: © Statistische Ämter des Bundes und der Länder, Regionaldatenbank Deutschland, Dezember 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (Daten geändert). Die Landkreisflächen werden auf Grundlage von Geodaten des Bundesamtes für Kartographie und Geodäsie berechnet: © GeoBasis-DE / BKG 2014 (Daten geändert).</p> <p>Für weitere Informationen bzgl. der Potentialflächen siehe VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S. 105-109.</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
<p>This dataset contains detections of echolocation clicks at two sites in the Hawaiian Archipelago. These sites are Hawaii and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into five-minute bins and each bin was given a class label. The data provided here is further binned at a daily level, where counts of a given class represent the number of five-minute bins within a given day that were labeled as that class. One file is provided per site, and files are in .csv format that can be read using any desired coding language. </p> <p>In addition to acoustic counts, values for environmental variables considered in the corresponding manuscript are provided in the CSV files. The final file in this dataset contains satellite-derived chlorophyll-a concentration values from NASA MODIS for the Hawaiian region (used to create Supplementary Fig. 1). Details on all variables and how they were accessed can be found in the manuscript and in the README file accompanying this dataset. </p>
Fig. 2, A in An Overview Of The Ecological Values Of Soumar Wetland On Waterbirds Diversity
Fig. 2, A — monthly trend of abundance and richness of waterbirds in the Soumar wetland (Setif, Algeria). Abundance = number of individuals and Taxa_S = number of species. B — cumulative effect between abundance and richness using the Gini measure of evenness (G').
Fig. 4 in An Overview Of The Ecological Values Of Soumar Wetland On Waterbirds Diversity
Fig. 4. Diversity profile of waterbirds alpha diversity for each month in the Soumar wetland (Setif, Algeria). α = 0 richness; α = 1 Shannon–Weaver index; α = 2 inverse Simpson index (1/D); and α = a high value approximates the Berger–Parker index.
Initial values for distribution function
<p>Contains initial conditions for turbulent channel flow simulations using Discrete Unified Gas Kinetic Scheme (DUGKS). Binary representation of distribution function values is stored in a column major format. Data resolution is 19x384x256x256 eight byte floating point (FP64) numbers.</p>
Wind Value Summer Seminar 2023 Kevin Campbell on Compliance Bonds
<p>A 20 minute video on MP4 format of a talk given by Kevin Campbell at the Wind Value, Industry Meets Academia Summer Seminar, held on 31st August 2023 in the Environmental Research Institute, University College Cork, Ireland.</p>
Fig. 2 in Feeding preference of adult females of ribbonfish Trichiurus lepturus through prey proximate-composition and caloric values
Fig. 2. Multivariate correspondence analysis considering the proximate-composition and prey species of Trichiurus lepturus in northern Rio de Janeiro State, Brazil. Cb: Chirocentrodon bleekerianus; Dp: Doryteuthis plei; Lg: Lycengraulis grossidens; Ph: Pellona harroweri; Pp: Peprilus paru; Tl: Trichiurus lepturus and Xk: Xiphopenaeus kroyeri. CAR = carbohydrate.
Fig. 1 in Feeding preference of adult females of ribbonfish Trichiurus lepturus through prey proximate-composition and caloric values
Fig. 1. Map of Brazil with Rio de Janeiro State and its northern coast, where adult female specimens of Trichiurus lepturus and their prey species were collected (21º18'S-22º25'S; until 50 m depth).
Figure 1 in Lessons from the WBF2020: extrinsic and intrinsic value of soil organisms
Figure 1. (A) The World Biodiversity Forum was held high up in the snowy Alps in Davos. Credit: Marianne Darbi. (B) Participants of the House of Commons style debate. Participants discussed whether they thought biodiversity was declining. Credit: Marten Winter
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B in Environmental correlates of the European common toad hybrid zone
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B. spinosus hybrid zone from the Atlantic coast (window 1) to the Mediterranean (window 17). Variables shown are those selected by a logistic regression analysis, with 'species' as dependent variable and explanatory variables available for selection as in table 1. Units are as in table 1; see also Hijmans et al. (2005). Values for B. bufo and B. spinosus are shown by small and large dots, respectively. Grey areas indicate that values for B. bufo are lower than for B. spinosus. The graph at the top left provides AUC model fit values along with major topographical references. Rectangles indicate stretches of the species contact for which the environmental models have good fit (AUC> 0.8), with consistent results indicated by green shadings. For the other windows with less than good model fit, signals are likely to be absent or void, either from poor sampling (window 1), the presence of rivers (windows 5–9, 13–14), or a thin or absent species' contact (windows 16–17) (see fig. 3).
Fat oxidation rates and cardiorespiratory responses during exercise in different subject populations with post-acute sequelae of SARS-CoV-2 infection: a comparison with normative percentile values
<p>INTRODUCTION: Post-acute sequelae of SARS-CoV-2 infection (PASC) presents a spectrum of symptoms following acute COVID-19, with exercise intolerance being a prevalent manifestation likely linked to disrupted oxygen metabolism and mitochondrial function. This study aims to assess maximal fat oxidation (MFO) and exercise intensity at MFO (FATmax) in distinct PASC subject groups and compare these findings with normative data.</p> <p>METHODS: Eight male subjects with PASC were involved in this study. The participants were divided in two groups: “endurance-trained” subjects (V̇O<sub>2</sub>max > 55 ml/min/kg) and “recreationally-active” subjects (V̇O<sub>2</sub>max < 55 ml/min/kg). Each subject performed a graded exercise test until maximal oxygen consumption (V̇O<sub>2</sub>max) to measure fat oxidation. Subsequently, MFO was assessed and FATmax calculated as the ratio between V̇O<sub>2 </sub>at MFO and V̇O<sub>2</sub>max.</p> <p>RESULTS: The MFO and FATmax of “endurance-trained” subjects were 0.85, 0.89, 0.71 and 0.42, and 68%, 69%, 64% and 53%, respectively. Three out of four subjects showed both MFO and FATmax values placed over the 80<sup>th</sup> percentile of normative data. The MFO and FATmax of “recreationally-active” subjects were 0.34, 0.27, 0.35 and 0.38, and 47%, 39%, 43% and 41%, respectively. All MFO and FATmax values of those subjects placed below the 20<sup>th</sup> percentile or between the 20<sup>th</sup> and 40<sup>th</sup> percentile.</p> <p>DISCUSSION: Significant differences in MFO and FATmax values between 'endurance-trained' and “recreationally-active” subjects suggest that specific endurance training, rather than simply an active lifestyle, may provide protective effects against alterations in mitochondrial function during exercise in subjects with PASC.</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.