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10,767 results for “Contribution”
2746-node Polish Energy System Data of Transmission and Voltage Constraints Contribution to the Formation of LMP
<p>This is the dataset that is used for the original article: "Contribution of Transmission and Voltage Constraints in the Formation of Locational Marginal Prices"</p> <p> </p> <p>MATPOWER is required. The dataset obtained by MATPOWER ver6.0 tool [1].</p> <p>Run run_matpower_acopf.m in MATLAB to start AC OPF. Offers should be saved in the MATLAB search path. Results of AC OPF are saved in bus, branch and gen files. Column names correspond to MATPOWER case file.</p> <p>Price-bonding factors are saved in lambda_P_PBF and lambda_Q_PBF. Ones in MP, MQ columns correspond to marginal nodes for real and reactive power respectively. Ones in CV, CD columns correspond to controlled voltage magnitude and phase respectively. </p> <p>Acronyms in column names:</p> <ul> <li>C(N) - bidding price of a marginal generator at node N </li> <li>Fmax(N) - maximum allowable real power throught line N, where N - index number of a line in branch.csv with binding constraint</li> <li>LAMP_P - LMP for real power</li> <li>LAM_Q - LMP for reactive power</li> <li>PBF - price-bonding factor</li> <li>TC - transmission constraint</li> <li>VC - voltage constraint</li> <li>V(N) - maximum (minimum) allowed voltages at node N</li> </ul>
Group Contribution Models for Tetrahymena Pyriformis Toxicity
<p>A data set of several models for Tetrahymena Pyriformis Toxicity (THPT) based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>)</p> <p>The main goal of the created THPT models is to illustrate Ambit-GCM software usage.</p> <p>Typically biological activity is not well modeled by means of additive schemes. In current modeling setup we demonstrate combination of group contribution scheme with external descriptors.</p>
Group Contribution Models for Standard molar enthalpy of formation of gas phase at 298.15K
<p>A data set of several models for Heat of formation based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>).</p> <p>QSPR models were built with a small training data set mainly for demonstration of Ambit-GCM software usage.</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"
<p>Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Mayrhofer_Neuron.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Mayrhofer_data_code.zip" (~20 GB) is a zipped version of a folder "Mayrhofer_data_code" (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named "MatlabCode", and the specific code for generating each figure panel is in a sub-subfolder named "Figures_tjM1_paper". When running the code, you need to set the Matlab file path to be "Mayrhofer_data_code". In addition, you should add the folder "Mayrhofer_data_code" with subfolders in Matlab "Set Path". The figures will be saved in a subfolder named "Figures". Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.</p>
TMEM206 contributes to cancer hallmark functions in colorectal cancer cells and is regulated by p53 in a p21-dependent manner
<p><span>Acid-induced ion flux plays a role in pathologies where tissue acidification is prevalent, including cancer. In 2019, TMEM206 was identified as the molecular component of acid-induced chloride flux. Localizing to the plasma membrane, TMEM206 contributes to cellular processes like acid-induced cell death. Since over 50% of human cancers carry loss of function mutations in the p53 gene, we aimed to analyze how TMEM206 is regulated by p53 and its role in cancer hallmark function and acid-induced cell death in HCT116 colorectal cancer (CRC) cells. We generated p53-deficient HCT116 cells and assessed TMEM206-mediated Cl<sup>-</sup> currents and transcriptional regulation using the patch-clamp and a dual-luciferase reporter assay, respectively. To investigate the contribution of TMEM206 to cancer hallmark functions we performed migration and metabolic activity assays. The role of TMEM206 in p53-mediated acid-induced cell death has been assessed with cell death assays. TMEM206 mRNA level is significantly elevated in human primary CRC tumors. TMEM206 knockout increased acid-induced cell death and reduced proliferation and migration, indicating a role for TMEM206 in these cancer hallmark functions. Furthermore, we observed increased TMEM206 mRNA levels and currents in HCT116 p53 knockout cells. This phenotype can be rescued by transient overexpression of p53, but not by overexpression of dysfunctional p53. In addition, our data suggests that TMEM206 may mediate cancer hallmark functions within p53-associated pathways. TMEM206 promoter activity is not altered by p53 overexpression. Conversely, knockout of p21, a major target gene of p53, increased TMEM206-mediated currents suggesting expression control of TMEM206 by p21 downstream signaling. Our results show that in colorectal cancer cells, TMEM206 expression is elevated, contributes to cancer hallmark functions and its regulation is dependent on p53 through a p21-dependent mechanism.</span></p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Nationally Determined Contributions (Latest submissions)
<p>Links to the latest (up to October 2021) national climate action plans under the Paris Agreement</p>
Residual dynamics resolves recurrent contributions to neural computation
<p>This repository contains all the neural and simulated datasets used in the above publication. All the data are stored as .mat files.</p> <p>This repository is organized into three main folders. For ease of use, we recommend extracting the contents of each of the three folders into a single folder titled ‘data’.</p> <ol> <li>neuraldata –Raw and preprocessed neural data. Organized into the following folders: <ol> <li>array_TDR_dotsTask_data – contains the non-processed neural data of each session for both monkeys</li> <li>array_dotsTask_beh_xx – contains the behavioral data of each session for both monkeys (xx = monkey)</li> <li>array_reppasdotsTask_datasegmented_binsize=xxms – contains the pre-processed neural data (session wise) of both monkeys for a specified bin size (xx = bin size)<br> </li> </ol> </li> <li>simulations – Data from the simulated models. Organized into the following folders: <ol> <li>toymodels – Simulated data from all models of decisions/movement. Also includes simulations of the augmented line attractor and rotational dynamics models.</li> <li>twoarearnn – Simulated data from the two types of (feedback and nofeedback) two-area RNN models, along with the impulse response simulations.<br> </li> </ol> </li> <li>analyses – contains processed data files from various stages of the analysis pipeline. Only the most important files are listed below: <ol> <li>xx_aligned_reppasdotsTask_binsize=45ms.mat – session-aligned neural data for a specified monkey (xx = monkey)</li> <li>xx_ndim=8_lag=3_alpha=200_50_allconfigsresdynresults.mat – contains the residual dynamics for a specified monkey.</li> <li>xx_aligned_hankelagdimCV_reppasdotsTask_binsize=45ms.mat and xx_aligned_smoothnessCV_reppasdotsTask_binsize=45ms.mat – results of cross-validation of the residual dynamics pipeline for a specified monkey (xx = monkey)</li> </ol> </li> </ol>
Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'
<p>Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e</p> <p>Principal Investigator: Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@ips.unibe.ch, ORCID: 0000-0002-4260-8706)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)<br> Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: November 2020 - August 2021<br> Location of data collection: Kathmandu, Nepal<br> Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal'</p> <p>R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep</p> <p>Data Overview:</p> <p>1. 'Literature_References.csv'<br> List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files.</p> <p><br> 2. 'Drivers_and_Trends.csv'<br> The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends.<br> The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP. <br> We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological). <br> If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1.</p> <p>3. 'NCP_to_SDG.csv'<br> The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article.<br> The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)"<br> The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards.<br> The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from.</p> <p>Codes for NCPS:<br> HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options).</p> <p>4. 'Data_consolidated.xlsx'<br> The above three data files combined into one xlsx document<br> </p> <p> </p> <p> </p>
Data from: Maximizing the potential for living cell banks to contribute to global conservation priorities
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Maximizing the potential for living cell banks to contribute to global conservation priorities</strong>" published in Zoo Biology. This study analyzed the representation of amphibian, bird, mammal and reptile species within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo® living cell collection (as of April 2019) and implemented a qualitative framework for the prioritization of species for future sampling. We used global conservation assessment schemes (including the IUCN Red List of Threatened Species<sup>TM</sup>, CITES, the Alliance for Zero Extinction, the EDGE of Existence, and Climate Change Vulnerability), and opportunities for sample acquisition from the global zoo and aquarium community, to identify priority species for future cryobanking efforts.</p> <p>Two datasets accompany this publication. The first provides a complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S1</strong>). The second provides a list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S2</strong>).</p> <p>Data provided by SDZWA are only available upon request. Data for each conservation assessment scheme were collected from each source individually (all freely available), and are accurate to April 2019. Zoo species holdings data were provided upon request by Species360 (https://www.species360.org/), which operates the real-time database ZIMS (Zoological Information Management System). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each species and their current population sizes. </p> <p> </p> <p><strong>Description of the Datasets</strong></p> <p> </p> <p><strong>Supporting Information Data S1 (accurate to April 2019):</strong> A complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> <li><strong>AZE:</strong> The listing of the species on the Alliance for Zero Extinction assessment (listed = 1, not listed = 0)</li> <li><strong>AZA:</strong> The presence of an active <em>ex situ</em> management programme for the species in the Association of Zoos and Aquariums (active programme = 1, no programme = 0)</li> <li><strong>EAZA: </strong>The presence of an active <em>ex situ</em> management programme for the species in the European Association of Zoos and Aquaria (active programme = 1, no programme = 0)</li> <li><strong>CITES:</strong> The listing of the species on of the three CITES appendices (listed = 1, not listed = 0)</li> <li><strong>CC Vulnerability: </strong>The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (vulnerable = 1, not vulnerable = 0)</li> <li><strong>EDGE: </strong>The listing of the species on the Evolutionary Distinctiveness and Global Endangerment assessment (listed = 1, not listed = 0)</li> </ul> <p> </p> <p><strong>Supporting Information Data S2 (accurate to April 2019):</strong> A list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>CC Vulnerability:</strong> The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (all species are vulnerable = H)</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (excluding VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We sincerely thank the San Diego Zoo Wildlife Alliance and Species360 member institutions for their support and data input. This research was funded by the Irish Fulbright Commission 2018/2019 to A.M., the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. D.A.C. and J.S. were funded by Species360, the University of Southern Denmark and the Species360 Conservation Science Alliance sponsors: the World Association of Zoos and Aquariums (WAZA), Mandai Wildlife Reserve, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data collected from the various conservation assessment schemes, and those contained within ZIMS, could contain errors or reporting failures. For example, ZIMS data are are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Ryder, O. A., Houck, M. L., Staerk, J., Conde, D. A., & Buckley, Y. M. (2023). Maximizing the potential for living cell banks to contribute to global conservation priorities. <em>Zoo Biology</em>, 1– 12. <a href="https://doi.org/10.1002/zoo.21787">https://doi.org/10.1002/zoo.21787</a></p>
Dataset for 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'
<p>Dataset for ACP manuscript 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'. Further explanations on the datasets can be found in the 'README.txt' file. In case of any questions please contact: Philipp Eger (eger@bafg.de), Federal Institute of Hydrology.</p> <p>1_Timeseries_Worms</p> <p>Description: Time series of measured gaseous and particulate species for station "BRI" in Worms on 10 December 2021. </p> <p><br> 2_All_ship_peaks_Worms</p> <p>Description: All analyzed ship peaks fulfilling the quality check criteria. </p> <p><br> 3_Ship_contribution_Worms</p> <p>Description: Mean monthly contribution from shipping based on analyzed peaks. </p> <p><br> 4_Examples_Worms</p> <p>Description: Particle size distribution of five exemplary peaks (A-E).</p>
Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies
<p>Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies</p> <p><strong>Abstract</strong></p> <p>Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Morphology contains the following files:</p> <p>wing_morpho.R<br> R scripts for data transformation of wing shape</p> <p>genital_morpho.R<br> R scripts for data transformation of genital morphology</p> <p><br> Models_used.R:<br> R scripts used to produce the statistical analyses.</p> <p>genital_morpho_master_with_pca.txt<br> Phenotypic data for genital morphology</p> <p>wing_contemporary_morpho_master_with_pca.txt<br> Phenotypic data for contemporary wing patterns</p> <p>wing_historic_morpho_master_with_pca.txt<br> Phenotypic data for wing patterns from museum samples</p> <p>The text files contains the following information:</p> <p>ID = Individual ID<br> genotyped_allopatric = was the individual genotyped<br> latitude<br> longitude<br> DATE = Date of collection<br> DAY = Day of collection<br> MONTH = Month of collection<br> YEAR = Year of collection<br> SPOT = Collection site<br> boxplotID = ID to reproduce boxplot order as used in the paper<br> colory = color code to plot<br> cycle = year cycle (2018/19 or 2020/21)<br> yeartype = even or odd year<br> genital_x_LM1 = linear measure of genital landmark 1 along the x axis<br> genital_y_LM1 = linear measure of genital landmark 1 along the y axis<br> genital_x_LM2 = linear measure of genital landmark 2 along the x axis <br> genital_y_LM2 = linear measure of genital landmark 2 along the y axis <br> genital_x_LM3 = linear measure of genital landmark 3 along the x axis <br> genital_y_LM3 = linear measure of genital landmark 3 along the y axis <br> genital_x_LM4 = linear measure of genital landmark 4 along the x axis <br> genital_y_LM4 = linear measure of genital landmark 4 along the y axis <br> genital_x_LM5 = linear measure of genital landmark 5 along the x axis <br> genital_y_LM5 = linear measure of genital landmark 5 along the y axis <br> v_t1 = length relationship between v and t1<br> v_t2 = length relationship between v and t2 <br> v_t3 = length relationship between v and t3 <br> t3_t1 = length relationship between t3_t1 <br> t3_t2 = length relationship between t3_t2 <br> t2_t1 = length relationship between t2_t1 <br> v_tg = length relationship between v and tg <br> PC1.x = PC1 axis for unprojected morphospace<br> PC2.x = PC2 axis for unprojected morphospace <br> PC3.x = PC3 axis for unprojected morphospace <br> PC4.x = PC4 axis for unprojected morphospace <br> PC5.x = PC5 axis for unprojected morphospace <br> PC6.x = PC6 axis for unprojected morphospace <br> PC7.x = PC7 axis for unprojected morphospace <br> PC1.y = PC1 axis for projected morphospace <br> PC2.y = PC2 axis for projected morphospace <br> PC3.y = PC3 axis for projected morphospace <br> PC4.y = PC4 axis for projected morphospace <br> PC5.y = PC5 axis for projected morphospace <br> PC6.y = PC6 axis for projected morphospace <br> PC7.y = PC7 axis for projected morphospace</p> <p> </p> <p><br> wing_ProcCoord1 = Procrustes coordinate 1<br> wing_ProcCoord2 = Procrustes coordinate 2<br> wing_ProcCoord3 = Procrustes coordinate 3<br> wing_ProcCoord4 = Procrustes coordinate 4<br> wing_ProcCoord5 = Procrustes coordinate 5<br> wing_ProcCoord6 = Procrustes coordinate 6<br> wing_ProcCoord7 = Procrustes coordinate 7<br> wing_ProcCoord8 = Procrustes coordinate 8<br> wing_ProcCoord9 = Procrustes coordinate 9<br> wing_ProcCoord10 = Procrustes coordinate 10<br> wing_ProcCoord11 = Procrustes coordinate 11<br> wing_ProcCoord12 = Procrustes coordinate 12<br> wing_ProcCoord13 = Procrustes coordinate 13<br> wing_ProcCoord14 = Procrustes coordinate 14<br> wing_ProcCoord15 = Procrustes coordinate 15<br> wing_ProcCoord16 = Procrustes coordinate 16<br> wing_ProcCoord17 = Procrustes coordinate 17<br> wing_ProcCoord18 = Procrustes coordinate 18<br> wing_ProcCoord19 = Procrustes coordinate 19<br> wing_ProcCoord20 = Procrustes coordinate 20<br> wing_ProcCoord21 = Procrustes coordinate 21<br> wing_ProcCoord22 = Procrustes coordinate 22<br> wing_ProcCoord23 = Procrustes coordinate 23<br> wing_ProcCoord24 = Procrustes coordinate 24<br> wing_ProcCoord25 = Procrustes coordinate 25<br> wing_ProcCoord26 = Procrustes coordinate 26<br> wing_ProcCoord27 = Procrustes coordinate 27<br> wing_ProcCoord28 = Procrustes coordinate 28<br> wing_ProcCoord29 = Procrustes coordinate 29<br> wing_ProcCoord30 = Procrustes coordinate 30<br> wing_ProcCoord31 = Procrustes coordinate 31<br> wing_ProcCoord32 = Procrustes coordinate 32<br> wing_ProcCoord33 = Procrustes coordinate 33<br> wing_ProcCoord34 = Procrustes coordinate 34<br> wing_ProcCoord35 = Procrustes coordinate 35<br> wing_ProcCoord36 = Procrustes coordinate 36<br> wing_ProcCoord37 = Procrustes coordinate 37<br> wing_ProcCoord38 = Procrustes coordinate 38<br> wing_ProcCoord39 = Procrustes coordinate 39<br> wing_ProcCoord40 = Procrustes coordinate 40<br> wing_ProcCoord41 = Procrustes coordinate 41<br> wing_ProcCoord42 = Procrustes coordinate 42<br> wing_ProcCoord43 = Procrustes coordinate 43<br> wing_ProcCoord44 = Procrustes coordinate 44<br> wing_ProcCoord45 = Procrustes coordinate 45<br> wing_ProcCoord46 = Procrustes coordinate 46<br> wing_ProcCoord47 = Procrustes coordinate 47<br> wing_ProcCoord48 = Procrustes coordinate 48<br> wing_ProcCoord49 = Procrustes coordinate 49<br> wing_ProcCoord50 = Procrustes coordinate 50<br> wing_ProcCoord51 = Procrustes coordinate 51<br> wing_ProcCoord52 = Procrustes coordinate 52<br> wing_ProcCoord53 = Procrustes coordinate 53<br> wing_ProcCoord54 = Procrustes coordinate 54<br> PC1.x = PC1 unprojected<br> PC2.x = PC2 unprojected<br> PC3.x = PC3 unprojected<br> PC4.x = PC4 unprojected<br> PC5.x = PC5 unprojected<br> PC6.x = PC6 unprojected<br> PC7.x = PC7 unprojected<br> PC8.x = PC8 unprojected<br> PC9.x = PC9 unprojected<br> PC10.x = PC10 unprojected<br> PC11.x = PC11 unprojected<br> PC12.x = PC12 unprojected<br> PC13.x = PC13 unprojected<br> PC14.x = PC14 unprojected<br> PC15.x = PC15 unprojected<br> PC16.x = PC16 unprojected<br> PC17.x = PC17 unprojected<br> PC18.x = PC18 unprojected<br> PC19.x = PC19 unprojected<br> PC20.x = PC20 unprojected<br> PC21.x = PC21 unprojected<br> PC22.x = PC22 unprojected<br> PC23.x = PC23 unprojected<br> PC24.x = PC24 unprojected<br> PC25.x = PC25 unprojected<br> PC26.x = PC26 unprojected<br> PC27.x = PC27 unprojected<br> PC28.x = PC28 unprojected<br> PC29.x = PC29 unprojected<br> PC30.x = PC30 unprojected<br> PC31.x = PC31 unprojected<br> PC32.x = PC32 unprojected<br> PC33.x = PC33 unprojected<br> PC34.x = PC34 unprojected<br> PC35.x = PC35 unprojected<br> PC36 = PC36 unprojected<br> PC37 = PC37 unprojected<br> PC38 = PC38 unprojected<br> PC39 = PC39 unprojected<br> PC40 = PC40 unprojected<br> PC41 = PC41 unprojected<br> PC42 = PC42 unprojected<br> PC43 = PC43 unprojected<br> PC44 = PC44 unprojected<br> PC45 = PC45 unprojected<br> PC46 = PC46 unprojected<br> PC47 = PC47 unprojected<br> PC48 = PC48 unprojected<br> PC49 = PC49 unprojected<br> PC50 = PC50 unprojected<br> PC51 = PC51 unprojected<br> PC52 = PC52 unprojected<br> PC53 = PC53 unprojected<br> PC54 = PC54 unprojected<br> PC1.y = PC1 projected<br> PC2.y = PC2 projected<br> PC3.y = PC3 projected<br> PC4.y = PC4 projected<br> PC5.y = PC5 projected<br> PC6.y = PC6 projected<br> PC7.y = PC7 projected<br> PC8.y = PC8 projected<br> PC9.y = PC9 projected<br> PC10.y = PC10 projected<br> PC11.y = PC11 projected<br> PC12.y = PC12 projected<br> PC13.y = PC13 projected<br> PC14.y = PC14 projected<br> PC15.y = PC15 projected<br> PC16.y = PC16 projected<br> PC17.y = PC17 projected<br> PC18.y = PC18 projected<br> PC19.y = PC19 projected<br> PC20.y = PC20 projected<br> PC21.y = PC21 projected<br> PC22.y = PC22 projected<br> PC23.y = PC23 projected<br> PC24.y = PC24 projected<br> PC25.y = PC25 projected<br> PC26.y = PC26 projected<br> PC27.y = PC27 projected<br> PC28.y = PC28 projected<br> PC29.y = PC29 projected<br> PC30.y = PC30 projected<br> PC31.y = PC31 projected<br> PC32.y = PC32 projected<br> PC33.y = PC33 projected<br> PC34.y = PC34 projected<br> PC35.y = PC35 projected</p> <p> </p> <p> </p> <p> </p> <p>Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795):</p> <p>all_euryale_calls.vcf.gz<br> The unfiltered VCF file</p> <p>euryale_V2.sh<br> Shell script for the genomic data analysis</p> <p>introgress.R<br> R script for running Introgress</p> <p>introgress_all_east2.txt<br> Output of Introgress for the Eastern contact zone</p> <p>introgress_all_west2.txt<br> Output of Introgress for the Western contact zone</p> <p>Admixture_output.txt<br> Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID</p> <p>Outliers2BombyxMori.txt<br> BLAST summary of outlier regions against Bombyx Mori</p> <p>Outliers2ManjolaJurtina.txt<br> BLAST summary of outlier regions against Manjola jurtina</p> <p>Outliers2ParargeAegeria.txt<br> BLAST summary of outlier regions against Pararge aegeria</p> <p> </p>
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
Artifact for the EASE 2020 Paper: How Can I Contribute? A Qualitative Analysis of Community Websites of 25 Unix-Like Distributions
<p>Artifact for the EASE 2020 Paper: How Can I Contribute? A Qualitative Analysis of Community Websites of 25 Unix-Like Distributions</p> <p>Jacob Krüger, Sebastian Nielebock, Robert Heumüller</p> <p> </p> <p>Please refer to the readme for more details</p>
Appendix 1 in Contribution to chromosome numbers and phylogeny of Turkish Vincetoxicum Wolf (Apocynaceae, Asclepiadoideae)
Appendix 1. Locality information, voucher specimens and accession numbers for sequences in GenBank (NCBI) of the examined specimens used for molecular (Mol.) and cytological (Cyt.) studies. Sequences previously published are indicated in brackets (A=Liede-Schumann et al. 2016, B=Liede- Schumann et al. 2012, C=Liede et al. 2002, D=Liede 2001, E=Berner & Carter Unpublished, F=Lahaye et al. 2005, G=Liede & Täuber 2002, H=Goyder et al. 2007, PS=present study).
Figures 7–13. Jansenia myanmarensis Wiesner, 2004. 7 in Two new tiger beetle species (Coleoptera: Cicindelidae) from Myanmar and notes on another species. 151. Contribution towards the knowledge of the Cicindelidae
Figures 7–13. Jansenia myanmarensis Wiesner, 2004. 7) Habitus, male, scale = 5 mm. 8–9. Labrum, scale = 1 mm. 8) Male. 9) Female. 10–12. Left elytron, scale = 2 mm. 10) Male. 11) Female. 12) Paratype female. 13) Left lateral view of aedeagus, scale = 1 mm.
ScienceDex guides
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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.