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4,230 results for “Energie”
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters
<p>CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters</p> <p>Collected and generated data for the paper</p> <p>## Data Tables</p> <p>Off-target score data for the ROC analysis using Haeussler dataset [2].</p> <p>Data from the table below is used to generate the Figure-2, Table-1 and Supplementary Figure-1 in the corresponding paper [1]. Don't forget to cite the corresponding studies as well if you use this table.</p> <ul> <li><strong>Haeussler_mm6_scores.csv.gz</strong>: This table includes the off-targeting scores of 1167036 off-target sequences, computed with CRISPRoff[1], CCTop[3], CFD[4], Cropit[5], Elevation (Elevation-score)[6], MIT[2,7] and VfoldCAS[8] methods. Off-target data has been taken from the Haeussler dataset [2].</li> </ul> <p>Analysis with CIRCLE-seq dataset [9]</p> <p>Data in all the three tables below has been generated to analyze the CIRCLE-seq dataset [9]. This data is further used to generate the Figure-3, Figure-4, and Supplementary Figure-4 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>CIRCLEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Figure-3 in the paper. It includes the 7 different off-targeting scores of CIRCLE-seq reported off-target sequences and the read counts from CIRCLE-seq experiments.</p> </li> <li> <p><strong>CIRCLEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Figure-4 in the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1)[10] based off-target predictions for CIRCLE-seq gRNAs.</p> </li> <li> <p><strong>CIRCLEseq_specificities.csv.gz</strong>: This table is used when generating the Supplementary Figure-4 in the supplementary document of the paper. It includes the specificty scores of CIRCLE-seq gRNAs, computed with CRISPRspec[1], MIT[2,7], MIT*[1,2,7] and Elevation (Elevation-aggregate)[6] methods.</p> </li> </ul> <p>Analysis with SITE-seq dataset [11]</p> <p>Data in all the three tables below has been generated to analyze the SITE-seq dataset [11]. This data is further used to generate the Figure-5, Supplementary Figure-2 and Supplementary Figure-3 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>SITEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-2 in the supplementary document of the paper. It includes the 7 different off-targeting scores of SITE-seq reported off-target sequences and the read counts from SITE-seq experiments.</p> </li> <li> <p><strong>SITEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-3 in the supplementary document of the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1) based off-target predictions for SITE-seq gRNAs.</p> </li> <li> <p><strong>SITEseq_specificities.csv.gz</strong>: This table is used when generating the Figure-5 in the paper. It includes the 4 different specificty scores of SITE-seq gRNAs.</p> </li> </ul> <p>Specificity-Efficiency Analysis</p> <p>This data is used to generate the Figure-6 and Supplementary Figure-5 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li><strong>Doench_Wang_specificity_grps.csv.gz</strong>: This table includes the specificity group of 3802 gRNA/on-target sequences, computed with CRISPRspec and MIT methods. gRNA sequence and modulation frequency data have been taken from the Haeussler dataset [2].</li> </ul> <p>## Citation</p> <p>If you find this data useful for your research, please cite the following works where appropriate:</p> <ol> <li>[Our citation comes here]</li> <li>Haeussler, M., Schonig, K., Eckert, H., Eschstruth, A., Mianne, J., Renaud, J.B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., Joly, J.S., Concordet, J.P.: Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17(1), 148 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/27380939">PMID 27380939</a></li> <li>Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J., Mateo, J.L.: CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS ONE 10(4), 0124633 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/25909470">PMID 25909470</a></li> <li>Doench, J.G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E.W., Donovan, K.F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H.W., Listgarten, J., Root, D.E.: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34(2), 184–191 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26780180">PMID 26780180</a></li> <li>Singh, R., Kuscu, C., Quinlan, A., Qi, Y., Adli, M.: Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 43(18), 118 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26032770">PMID 26032770</a></li> <li>Listgarten, J., Weinstein, M., Kleinstiver, B.P., Sousa, A.A., Joung, J.K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J.G., Fusi, N.: Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nature Biomedical Engineering 2, 38–47 (2018). <a href="https://www.ncbi.nlm.nih.gov/pubmed/29998038">PMID 29998038</a></li> <li>Hsu, P.D., Scott, D.A., Weinstein, J.A., Ran, F.A., Konermann, S., Agarwala, V., Li, Y., Fine, E.J., Wu, X., Shalem, O., Cradick, T.J., Marraffini, L.A., Bao, G., Zhang, F.: DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31(9), 827–832 (2013). <a href="https://www.ncbi.nlm.nih.gov/pubmed/23873081">PMID 23873081</a></li> <li>Xu, X., Duan, D., Chen, S.J.: CRISPR-Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment. Sci Rep 7(1), 143 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28273945">PMID 28273945</a></li> <li>Tsai, S.Q., Nguyen, N.T., Malagon-Lopez, J., Topkar, V.V., Aryee, M.J., Joung, J.K.: CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14(6), 607–614 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459458">PMID 28459458</a></li> <li>Alkan, F., Wenzel, A., Palasca, O., Kerpedjiev, P., Rudebeck, A.F., Stadler, P.F., Hofacker, I.L., Gorodkin, J.: RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets. Nucleic Acids Res. (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28108657">PMID 28108657</a></li> <li>Cameron, P., Fuller, C.K., Donohoue, P.D., Jones, B.N., Thompson, M.S., Carter, M.M., Gradia, S., Vidal, B., Garner, E., Slorach, E.M., Lau, E., Banh, L.M., Lied, A.M., Edwards, L.S., Settle, A.H., Capurso, D., Llaca, V., Deschamps, S., Cigan, M., Young, J.K., May, A.P.: Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods 14(6), 600–606 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459459">PMID 28459459</a></li> </ol> <p>## Contact</p> <p>ferro@rth.dk gorodkin@rth.dk</p>
New Azo-DMOF-1 MOF as a photo-responsive low-energy CO2 adsorbent and its exceptional CO2/N2 separation performance in mixed matrix membranes
<p>Data repository for manuscript, published in <em>ACS Applied Materials & Interfaces</em>, <strong>2018</strong>, <em>10</em> (40), pp 34291–34301, <a href="https://dx.doi.org/10.1021/acsami.8b12261">http://dx.doi.org/10.1021/acsami.8b12261</a></p> <p><strong>Abstract</strong></p> <p>A new generation-2 light-responsive metal–organic framework (MOF) has been successfully synthesized using Zn as the metal source and both 2-phenyldiazenyl terephthalic acid and 1,4-diazabicyclo[2.2.2]octane (DABCO) as the ligands. It was found that Zn-azo-dabco MOF (Azo-DMOF-1) exhibited a photoresponsive CO<sub>2</sub> adsorption both in static and dynamic condition because of the presence of azobenzene functionalities from the ligand. Further application of this MOF was evaluated by incorporating it as a filler in a mixed matrix membrane for CO<sub>2</sub>/N<sub>2</sub> gas separation. Matrimid and polymer of intrinsic microporosity-1 (PIM-1) were used as the polymer matrix. It was found that Azo-DMOF-1 could enhance both the CO<sub>2</sub> permeability and selectivity of the pristine polymer. In particular, the Azo-DMOF-1–PIM-1 composite membranes have shown a promising performance that surpassed the 2008 Robeson Upper Bound.</p>
Drug-membrane transfer free energies for coarse-grained trimers and tetramers
<p>The databases contain drug-membrane transfer free energies for coarse-grained Martini trimers and tetramers inserted in a one-component DOPC membrane. We also provide a database of atomistic-resolution compounds mined from the GDB and that map to trimers.</p>
The Diurnal Cycle of Integrated Kinetic Energy and Wind Radii in a Simulated Tropical Cyclone
<p>Model source code and output of a 340-day-long Cloud Model 1 simulation of a tropical cyclone and associated post-processing scripts.</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
A set of synthetic spectral energy distributions for "diskless", intermediate-mass young stars
<p>These models were published in conjunction with <em>The Duration of Star Formation in Galactic Giant Molecular Clouds. I. The Great Nebula in Carina, </em>by <a href="https://arxiv.org/abs/1906.01730">Povich et al. (2019)</a>. They will also be used in subsequent papers in that series.</p> <p>The format of these models conforms with the standards of <a href="https://doi.org/10.1051/0004-6361/201425486">Robitaille (2017)</a>, so they are compatible with the <a href="http://sedfitter.readthedocs.io/en/stable/installation.html">python implementation</a> of the <a href="https://doi.org/10.1086/512039">Robitaille et al. (2007)</a> SED fitting tool. We have pre-convolved these models with a number of useful filters, including Johnson/Bessel <em>UB</em><em>VRI, </em>UKIRT <em>ZYJHK, </em>VISTA <em>ZYJHK<sub>S</sub></em>, 2MASS <em>JHK<sub>S</sub></em>, <em>Spitzer/</em>IRAC and MIPS. </p> <p>A software pipeline implementing these models to constrain the age and mass distributions of young stellar populations is also <a href="https://doi.org/10.5281/zenodo.3234101">publicly available</a>.</p> <p><strong>Limitations of these models</strong></p> <p><em>These models produce the best results for stars more massive than the Sun and older than about 0.5 Myr. </em>They employ the pre-main-sequence evolutionary tracks of <a href="https://arxiv.org/abs/astro-ph/0003477">Siess et al. (2000)</a> and <a href="https://ui.adsabs.harvard.edu/abs/1996A&A...307..829B/abstract">Bernasconi & Maeder (1996)</a>. Numerous modern tracks offer significant improvement in the treatment of subsolar-mass stars. In addition, the Kurucz stellar atmospheres used in these synthetic SEDs work best for T<sub>eff </sub>> 4,000 K; for cooler temperatures other models, for example the PHOENIX photospheres, may be more appropriate.</p> <p>Newer evolutionary tracks covering the intermediate-mass range are now available, for example the Geneva pre-MS tracks of <a href="https://doi.org/10.1051/0004-6361/201935051">Haemmerlé et al. (2019)</a>. The principal innovation of these modern models is the treatment of accretion and location of the intermediate-mass stellar birthline. The coolest, most luminous models in this set are likely <em>unphysical</em>, representing fully-convective stars of >2 solar masses and <0.5 Myr isochronal age.</p>
Energy Consumption of IO APIS (ESEM'2019 paper)
<p>## About the experiments</p> <p>### Acronyms used in the figures</p> <p>- BufferedReader (BR).<br> - LineNumberReader (LNR).<br> - CharArrayReader (CAR).<br> - PushbackReader (PBR).<br> - FileReader (FR).<br> - FileInputStream (FIS).<br> - BufferedInputStream (BIS).<br> - StringReader (SR).<br> - PushbackInputStream (PBIS).<br> - StringBufferInputStream (SBIS).<br> - ByteArrayInputStream (BAIS).<br> - LineNumberInputStream (LNIS).<br> - Scanner (SCN).<br> - O método readAllLines da classe Files, e um Stream de String (RFAL).<br> - O método lines da classe Files, e um Stream de String (RFL).<br> - O método newBufferedReader da classe Files, e um Stream de String (BRFL)<br> - BufferedWriter (BW).<br> - FileWriter (FW).<br> - StringWriter (SW).<br> - PrintWriter (PW).<br> - CharArrayWriter (CAW).<br> - FileOutputStream (FOS).<br> - ByteArrayOutputStream (BAOS).<br> - BufferedOutputStream (BOS).<br> - PrintStream (POS).</p> <p>### Settings</p> <p>For each setting, we experimented with three files of different sizes: 1 Mb, 10 Mb e 20 Mb.</p> <p>The experiments were ran in the following machine</p> <p>- Hardware: Intel® Core™ i7-2670QM CPU @ 2.20GHz, with 4 processors, 16GB DDR3 1600MHz, Ubuntu 16.04 LTS (kernel 4.4.0-112-generic).<br> - Java: Java(TM) SE Runtime Environment, version 1.8.0-151</p>
Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting
<p>This package contains the data used in the research article: "Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting" published in Biogeosciences journal.</p> <p>Changes in this version:</p> <p>Chamber_data.xlsx is now named Chamber_data_clearcut.xlsx. CO2 fluxes were also corrected.</p> <p>Added daily mean CO2, CH4 and N2O fluxes measured at the control site.</p> <p> </p> <p>Chamber_data_clearcut.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the clearcut site.</p> <p>Chamber_data_control.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the control site.</p> <p>EC_CO2_fluxes.xlsx contains the gapfilled 30-min mean CO2 fluxes (NEE) and its components (GPP and respiration).</p> <p>Energy_fluxes.xlsx contains the gapfilled hourly mean energy fluxes.</p> <p>Meteo_data.xlsx contains the daily means of the meteorological variables used in the study.</p>
SmartLife smart clothing gamification to promote energy-related behaviours among adolescents
<p>Inactivity and high sedentary behavior among adolescents are main societal problems. Unhealthy lifestyles place a large burden on society and promoting healthy lifestyles is thus key for health, wellness and economic prosperity. These non-communicable diseases and unhealthy lifestyles furthermore occur more often among lower socio-economic groups, which indicates a need for healthy lifestyle promotion programs to help reduce health inequalities and improve social inclusion. The SmartLife project aims to create a mobile game that requires lower body movement, and is personalized by physiological feedback measured by smart textiles. Personalization via smart textiles can present a game challenge achievable for the current fitness level of the player and can adjust this based on activity levels during game play. This approach can improve current exergames to achieve a higher level of intensity in physical activity, needed to create a health impact, and can do so considering what is achievable for the person and hence reduce drop-out and injury risks.</p>
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
Product Datasheets of MIDE Piezoelectric Energy Harvesters V20W & V25W And Product Datasheet of Brüel & Kjaer LDS 406-408 Electromagnetic Shaker
<p>Product datasheets containing technical information used for products' technical analyses as vibration energy harvesters:</p> <ul> <li>Product Datasheet LDS V406 and V408 shakers, Brüel & Kjaer, Nærum, Denmark (2012).</li> <li>Product Datasheet Volture Piezoelectric Energy Harvesters (including V20W and V25W), Midé Technology Corporation, Massachusetts, USA, rev. no. 002 ed. (2013).</li> <li>Product Datasheet for Materials Properties of Volture Piezoelectric Products (including V20W and V25W), Midé Technology Corporation, Massachusetts, USA (Retrieved 2013).</li> </ul>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Energy and source of a natural ball lightning based on observational facts
<p><span>The origin data about all figures </span><span>of this work (Energy and source of a natural ball lightning based on observational facts).</span></p>
Examining the oil price and renewable energy price nexus: Comparative wavelet analysis for the aftermath of 2008 financial crisis, shale oil crisis and COVID-19 pandemic.
<p><span>In this study, we conducted a wavelet analysis on the dependence between renewable energy indices and Brent oil index (Brent) for the period of 21st November, 2003 till 24th May, 2024. The objective of the paper includes comparing the co-movement of renewable energy stock prices and oil prices during three different crises including the global financial crisis, shale oil crisis and the covid-19 pandemic. We found that the dependence is similar for both Europe and on a global scale during the pre-crises time, where renewable energy prices lead Brent oil prices in the short and medium term. Furthermore, results confirm that there is substitutability between oil prices and renewable energy prices before all crises which shows a positive correlation. The results further show that the short and medium term dependence disappears after the oil crisis and financial crisis which is supported by the sudden loss in demand for oil. These findings show that co-movement changes between the three crises where there is no dependence between the indices after the financial and oil crisis while there is a negative correlation after the covid-19 pandemic. These findings could have significant ramifications for investors seeking to mitigate risks and for policymakers making decisions about supporting the advancement of renewable energy while understanding the change of behaviour between the two crises.</span></p>
Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities
<p>Supplementary data to the manuscript “Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities” by Elisabeth Salomon and Paula Toroi</p>
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses
<p>This archive includes the data used (e.g. Time use survey (UK-TUS) data), model files (idf files for running EnergyPlus) and codes for analysis in the paper (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.enbuild.2024.114668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.enbuild.2024.114668</a>).</p> <p>Files in this archive should include:</p> <ul> <li>Time use survey data analysis</li> </ul> <p>o Main dataset: TUS_activity.zip</p> <p>o Code: TUS_clustering_code.zip</p> <p>o Output: InternalHeatProfile.zip</p> <ul> <li>Building energy modeling </li> </ul> <p>o Main dataset (run in EnergPlus 9.4): IDFfiles.zip</p> <p>o Output: Eplus_output.zip</p> <ul> <li>PostProcess analysis</li> </ul> <p>o Code: QF_analysis_code.zip</p> <p>o Output: QF_output.zip</p> <p> </p> <p>Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.</p>
Potential energy surfaces and rovibrational line lists for cyclopropenethione
<p>Molpro restart files (ASCII) for the XSURF program of the potential energy and dipole moment surfaces of cyclopropenethione. Rovibrational line list (ASCII) obtained from RVCI calculations. Data refer to the publication <em>Hunting for sulfur-containing molecules in space: a spectroscopic characterization of cyclopropenethione based on high-level ab initio calculations </em>(https://doi.org/10.3847/1538-4357/ad73a0).</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
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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.
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DANDI Archive for NWB datasets
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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.