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1,888 results for “Cooperation”
Cooperative Alaska Forest Inventory (CAFI): I - Tree Inventory Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the tree data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): II - Seedling Inventory Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the seedling data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): III - Vegetation Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the vegetation data of the CAFI. The protocol has been changed in 2021. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): IV - Sapling Inventory Data 2022-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the sapling data of the CAFI. Sapling data is only available after 2022 due to a protocol change. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): V - Photo Collection 2022-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the collection of photos taken at the sites.
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Data for: Increasing plant group productivity through latent genetic variation for cooperation
<p>Historic yield advances in the major crops have to a large extent been achieved by selection for improved productivity of groups of plant individuals such as high-density stands. Research suggests that such improved group productivity depends on “cooperative” traits (e.g., erect leaves, short stems) that – while beneficial to the group – decrease individual fitness under competition. This poses a problem for some traditional breeding approaches, especially when selection occurs at the level of individuals, because “selfish” traits will be selected for and reduce yield in high-density monocultures. One approach, therefore, has been to select individuals based on ideotypes with traits expected to promote group productivity. However, this approach is limited to architectural and physiological traits whose effects on growth and competition are relatively easy to anticipate.</p> <p>Here, we developed a general and simple method for the discovery of alleles promoting cooperation in plant stands. Our method is based on the game-theoretical premise that alleles increasing cooperation benefit the monoculture group but are disadvantageous to the individual when facing non-cooperative neighbors. Testing the approach using the model plant <em>Arabidopsis thaliana</em><em>, </em>we found a major effect locus where the rarer allele was associated with increased cooperation and productivity in high-density stands. The allele likely affects a pleiotropic gene, since we find that it is also associated with reduced root competition but higher resistance against disease. Thus, even though cooperation is considered evolutionarily unstable except under special circumstances, conflicting selective forces acting on a pleiotropic gene might maintain latent genetic variation for cooperation in nature. Such variation, once identified in a crop, could rapidly be leveraged in modern breeding programs and provide efficient routes to increase yields.</p>
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <ul> <li>Figure-2-KBM-1-Queen Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb Comb and Brood -related Key Behavioural Metrics </li> </ul> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e., https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> <p>Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <ul> <li>KBM-1-performance evaluation: Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation: Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation: Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> <p>Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system:</p> <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag - queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag - queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag - worker bee trophylaxis (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag - worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
Data from: Competition among eggs shifts to cooperation along a sperm supply gradient in an external fertilizer
Purple sea urchin, Strongylocentrotus purpuratus, are broadcast spawners. Empirical and theoretical exploration of this trade-off in broadcast spawners has focused heavily on the role of sperm availability. In contrast, the particular manner in which egg concentrations alter both fertilization and polyspermy remains less explored. These data are from a laboratory experiment with a factorial design (six sperm concentrations x four egg concentrations replicated across multiple male-female pairs), to measure fertilization and polyspermy of purple sea urchins. Data were used to evaluate the impact of egg concentration on fertilization, and to explicitly test for random versus nonlinear sperm-egg collision rates. A new dynamic model was developed that expands upon existing models. Observations of fertilization and polyspermy were also used to parameterize and compare the performance of existing models that included random or or nonlinear collision parameters by utilizing several different basic model forms. For more information, see paper with published results or data set methods. Results for these data are pulished in Okamoto. D. K. 2016 Competition among eggs shifts to cooperation along a sperm supply gradient in an external fertilizer. The American Naturalist. 187:5, E129-E142. DOI: 10.1086/685813
Cooperative Proactive resource management for 5G in the unlicensed spectrum open data
<p>The data set consists of the following files:</p> <p><strong>1)COT information:</strong> The channel occupancy time of each channel for the first 5000 measurements. The COT values range from 0 to 1.</p> <p><strong>2)QL decisions uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider uniform traffic generation patterns.</p> <p><strong>3)QL decisions NON uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider non-uniform traffic generation patterns.</p> <p><strong>4)Performance measurements: </strong>The final results of the experiment in respect to the transmit power control and throughput measurements under different QL configurations.</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a network approach
<p>Dataset associated to Ramírez-Ramírez RD, Montilla LM, Cavada-Blanco F and Croquer A. Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a collaboration network approach [version 1; not peer reviewed]. <em>F1000Research</em> 2016, <strong>5</strong>:799 (poster) (doi: 10.7490/f1000research.1111809.1)</p>
Strongly Enhanced Cooperative Surface Propensity of Atmospherically Relevant Organic Molecular Ions in Aqueous Solution - data
<p>Dataset pertaining to the manuscript "Boosting aerosol surface effects: strongly enhanced cooperative surface propensity of atmospherically relevant organic molecular ions in aqueous solution", published in <a href="https://doi.org/10.5194/acp-25-3503-2025">Atmos. Chem. Phys., 25, 3503–3518, 2025</a>. Using liquid-jet photoelectron spectroscopy, we investigate the surface propensity of various carbonaceous species in aqueous solution. We cover a range of substances relevant to atmospheric climate models. Here we give the data of Fig.s 1-3 of our manuscript in numeric form, and document the underlying photoemission spectra including all relevant metadata.</p> <p>Experimental data are documented in the NeXus format (extension .nxs). For a description see:<br>The NeXus Data Format definition (v2024.02), https://manual.nexusformat.org/index.html<br>NXmpes expansion for FAIRmat data (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/classes/contributed_definitions/NXmpes.html<br>NXmpes_liquid expansion to NXmpes (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/mpes-liquid/classes/contributed_definitions/NXmpes_liquid.html</p> <p>The following files are provided:<br>'Data Collection_Core.nxs' - Photoemission data, core level spectra<br>'Data Collection_Valence.nxs'<strong> </strong> - Photoemission data, valence spectra</p> <p>Ascii data of figures 1a, 2 and 3:<br>'Figure 1 data.txt'<br>'Figure 2 data.txt'<br>'Figure 3 data.txt'</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we kindly ask you to send us a copy of your published results.</p> <p>Acknowledgements: We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III, and we would like to thank Moritz Hoesch and his team for assistance in using beamline P04. Beamtime was allocated for proposal I-20220937 EC. Harmanjot Kaur and Bernd Winter acknowledge the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 883759, AQUACHIRAL). Stephan Thürmer acknowledges support from JSPS KAKENHI (grant no. JP20K15229) and ISHIZUE 2024 of Kyoto University. Florian Trinter acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 509471550, Emmy Noether Programme. Florian Trinter and Bernd Winter acknowledge support by the MaxWater initiative of the Max-Planck-Gesellschaft. Olle Björneholm acknowledges support from the Swedish Research Council (VR) through project 2023-04346 and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) through project 202100-2932. Ricardo Marinho, Joel Pinheiro, and Arnaldo Naves de Brito acknowledge support from the Swedish–Brazilian collaboration STINT-CAPES (process no. 88881.465527/2019-01). Arnaldo Naves de Brito acknowledges support from FAPESP (the São Paulo Research Foundation, process no. 2017/11986-5), Shell and ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency), and CNPq-Brazil (process no. 401581/2016-0). Harmanjot Kaur and Shirin Gholami acknowledge support by the IMPRS for Elementary Processes in Physical Chemistry.</p> <p>Financial support: This research has been supported by the European Research Council, Horizon Europe (grant no. 883759); the Japan Society for the Promotion of Science (grant no. JP20K15229); the Deutsche Forschungsgemeinschaft (grant no. 509471550); the Vetenskapsrådet (grant no. 2023-04346), the Swedish Foundation for International Cooperation in Research and Higher Education (grant no. 202100-2932); the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant no. 2017/11986- 5); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (grant no. 401581/2016-0).</p> <p>Version history:<br>1 - initial release<br>2 - numbering of figures adapted to published version, photoemission data added.</p>
Data: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)
<p>This data package has been used to examine the responses of UK butterfly species </p> <p>to different features of the urban environment. 'JC_WCBSmodel.Rdata' presents the</p> <p>butterfly abundance data, and supporting information about </p> <p>species and sites. This data can be fed through the script '04_model_builder.R', to </p> <p>produce the models reported in the research article. '00_functions.R' is a script </p> <p>containing functions which support the modelling process, which is loaded as part of </p> <p>the 04_model_builder script. </p> <p> </p> <p>Summaries of the resulting models are an output of that script - </p> <p>'Butterfly_GAM_Outputs.xlsx'. These are represented graphically in the manuscript, </p> <p>using scripts '06_01_Map'.R:'06_03_Cross_Validation'. '06_04_Model_Metric.R' </p> <p>is a further summary of the .xlsx file, found in the Supplementary Materials. </p> <p>'06_05_graphic_4_twitter.R' produces a condensed version of the figure resulting </p> <p>from the script '06_02_Metric_Summary.R'</p> <p> </p> <p>Dataset descriptions are found in the attached readme.txt</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)
<p>The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>).</p> <p>The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3. </p> <p>All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line. The interconnected system is fully represented in "Network_XXX.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_XXX);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected.. The generated orderbook is presented in "OrderbookTN_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Simulation Parameters for Two Cooperative Binding Sites Sensitize PI(4,5)P2 Recognition by the Tubby Domain
<p>Dataset to perform the coarse-grained MD simulations presented in "Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain". The dataset includes protein structures and GROMACS simulation files such as mdp, itp, gro, and index files for the tubby domain as well as PLC-delta1 PH domain.</p>
Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"
<p>Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"</p> <p>This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Cooperative Binding and Chirogenesis in an Expanded Perylene Bisimide Cyclophane
<p>Additional data to report <a title="DOI URL" href="https://doi.org/10.1021/jacs.4c08073">https://doi.org/10.1021/jacs.4c08073</a>:</p> <p>The encapsulation of more than one guest molecule into a synthetic cavity is a highly desirable yet a highly challenging task to achieve for neutral supramolecular hosts in organic media. Herein, we report a neutral perylene bisimide cyclophane, which has a tailored chiral cavity with an interchromophoric distance of 11.2 Å, capable of binding two aromatic guests in a π-stacked fashion. Detailed host–guest binding studies with a series of aromatic guests revealed that the encapsulation of the second guest in this cyclophane is notably more favored than the first one. Accordingly, for the encapsulation of the coronene dimer, a cooperativity factor (α) as high as 485 was observed, which is remarkably high for neutral host–guest systems. Furthermore, a successful chirality transfer, from the chiral host to encapsulated coronenes, resulted in a chiral charge-transfer (CT) complex and the rare observation of circularly polarized emission originating from the CT state for a noncovalent donor–acceptor assembly in solution. The involvement of the CT state also afforded an enhancement in the luminescence dissymmetry factor (<em>g</em><sub>lum</sub>) value due to its relatively large magnetic transition dipole moment. The 1:2 binding pattern and chirality-transfer were unambiguously verified by single-crystal X-ray diffraction analysis of the host–guest superstructures.</p>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition
<p>This repository contains code to accompany the manuscript titled</p> <p><strong>Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition</strong></p> <p>by <strong>Carly D. Kenkel and Line K. Bay</strong><br> </p> <p>In this study, we used a phylogenetically controlled design to investigate the role of vertical symbiont transmission, an evolutionary mechanism predicted to enhance cooperation and holobiont fitness of reef-building corals. Six species of coral, three vertical transmitters and their closest horizontally transmitting relatives, were fragmented and subjected to a two-week thermal stress experiment. Symbiont cell density, photosynthetic function and translocation of photosynthetically fixed carbon between symbionts and hosts were quantified to assess changes in physiological metrics of fitness and cooperation. Amplicon sequencing of the <em>Symbiodinium</em> ITS-2 locus was used to investigate differences in symbiont community composition among focal species. We did not observe universally higher levels of cooperation in vertically transmitting species. However, the reduction in cooperation at the onset of bleaching was marginally associated with symbiont community diversity. Analysis of ITS2 amplicon sequence data suggest that it may not be vertical transmission <em>per se</em> that influences host-symbiont cooperation, but genetic uniformity of the symbiont community.</p> <p>Repository contents:</p> <ul> <li> <p><strong>TraitDataAnalysis.R:</strong> Annotated R script for generating figures and re-creating statistical analyses</p> <ul> <li> <p><strong>RsquaredGLMM.R:</strong> Accessory R script for running RsquaredGLMM analyses, called by <strong>TraitDataAnalysis.R</strong></p> </li> <li> <p><strong>NSF_RunningPam.csv</strong>: Input file for statistical analysis. Contains photophysiological data. Column headers are as follows:</p> <ul> <li> <p>Tank: Number of experimental tank in which experimental coral fragment was held</p> </li> <li> <p>Treatment: short-hand notation for sample treatments (e.g. ctrl1-5 = control temperature, genotypes 1-5)</p> </li> <li> <p>Water: source sump for temperature controlled water jackets for each set of treatment tanks</p> </li> <li> <p>Position: numerical rack position of coral fragment within experimental treatment tank</p> </li> <li> <p>Species: Coral species (Amil=<em>A. millepora</em>, Maqe=<em>M. aequituberculata</em>, Gast=<em>G. astreata</em>, Gach=<em>G. acrhelia</em>, Plob=<em>P. lobata</em>, Gcol=<em>G. columna</em>)</p> </li> <li> <p>Genotype: source colony origin of individual coral fragments within species</p> </li> <li> <p>Temp: experimental temperature treatment (ctrl: 27°C ; heat: 31°C)</p> </li> <li> <p>Treat: whether experimental corals received C14-labeled bicarbonate (bicarb), artemia or were sampled separately for Gene Expression Analysis (not presented in this manuscript)</p> </li> <li> <p>EQY: Effective quantum yield of <em>Symbiodinium</em> photosystem II as measured using PAM fluorometry</p> </li> <li> <p>Date: Actual calendar date of measure</p> </li> <li> <p>Transmission: coral symbiont transmission mode</p> </li> <li> <p>Reef: reef site of original coral collection</p> </li> <li> <p>Date: experimental date of measure</p> </li> </ul> </li> <li> <p><strong>TraitData.csv:</strong> Input file for statistical analysis. Contains all physiological trait data.</p> <ul> <li> <p>Includes columns as described above for the Running_Pam file in addition to columns containing raw trait data as described in the manuscript.</p> </li> </ul> </li> <li> <p><strong>TraitData_DaysAsCols.csv:</strong> Reformatted input file with trait data split by sampling day across columns</p> </li> </ul> </li> <li> <p><strong>DADA2Analysis.R:</strong> Annotated R script for generating figures and running ITS2 amplicon analyses</p> <ul> <li> <p>GeoSymbio_ITS2_LocalDatabase_verForPhyloseq.fasta: FASTA file of the GeoSymbio ITS2 reference database <a href="https://sites.google.com/site/geosymbio/">https://sites.google.com/site/geosymbio/</a>, formatted for use with the R prograom Phyloseq</p> </li> <li> <p>SeqVars_6Feb.fasta: FASTA file of identified sequence variants resulting from DADA2 analysis</p> </li> <li> <p>OutputDADA_6Feb.csv: Counts of sequence variants by sample</p> </li> <li> <p>Raw FASTQ paired end read files can be downloaded from NCBI's SRA: PRJNA338365</p> </li> </ul> </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.
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.