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8,998 results for “Adaptation”
Database of best practice for pondscape NbS for CC adaptation and mitigation
<p>We built an inventory (database) of Nature-based Solutions (NbS) actions (creation, restoration, and management) in ponds and pondscapes (ponds at the landscape scale) in a diversity of social-ecological settings to assess the best practices. We formulated an online questionnaire that was shared with pond stakeholders. The questionnaire asked general (e.g., number of ponds, area of the pondscape, etc.) and specific (e.g., costs of the action, stakeholders involved, etc.) information on the NbS action implemented, and on 11 associated Nature's Contributions to People (NCPs). Among the NCPs we included, for instance, habitat creation for biodiversity, regulation of climate, learning or physical and physiological experiences. The database contains information gathered through the questionnaire, research papers and relevant web pages and platforms.</p> <p>We used three different approaches to obtain information on NbS actions implemented in ponds/pondscapes and the associated NCPs mainly focusing on Europe and Uruguay: 1) the development of a user-friendly online questionnaire on NbS implemented in ponds/pondscapes and associated NCPs, which was shared in the form of a survey through the platform Survey Monkey with PONDERFUL members and pond Stakeholders; 2) the search of information in research papers; and 3) the search of information on web pages such as <a href="https://oppla.eu/" target="_blank" rel="noopener">https://oppla.eu</a>, <a href="https://renature-project.eu/" target="_blank" rel="noopener">https://renature-project.eu</a>, <a href="https://climate-adapt.eea.europa.eu/" target="_blank" rel="noopener">https://climate-adapt.eea.europa.eu</a>, <a href="https://una.city/" target="_blank" rel="noopener">https://una.city</a>. We requested permissions from the respondents to make the data available.</p>
Credit for software creators: An adapted illustration from The Turing Way: Shared under CC-BY 4.0 for reuse
<p>Illustration adapted from <strong>The Turing Way Community, & Scriberia. (2023). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. <a href="https://doi.org/10.5281/zenodo.8169292">https://doi.org/10.5281/zenodo.8169292</a></strong>.</p> <p>The illustration has been adapted by Stephan Druskat (subsumed as co-author in <em>The Turing Way Community</em>):</p> <ul> <li>The speech bubble has been changed to read "More credit" instead of "More credit<strong>s</strong>". (One character removed and alignment adapted.)</li> <li>The inscription on the purse has been changed to read "Credit" instead of "Credit<strong>s</strong>". (One character removed and alignment adapted.)</li> </ul>
DELTA: Dense Electromyography for Long-Term Adaptive control
<p>The <em><strong>DELTA </strong></em>dataset, namely ”<strong>D</strong>ense <strong>E</strong>lectromyography for <strong>L</strong>ong-<strong>T</strong>erm <strong>A</strong>daptive control”, holds significance in the realm of prosthetic applications, featuring High Density surface Electromyography data collected over an extended duration. It serves as a valuable resource for training data-driven models and testing them under conditions that closely emulate real-world prosthetic applications. Recognizing the temporal variability in EMG data, constructing models that are agnostic to such fluctuations could significantly enhance the efficacy of machine learning models in prosthetic applications</p>
Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"
<p>This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts. </p> <p>A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu). </p> <p>We have updated the post-process codes to reflect changes in the revised manuscript</p> <p>==</p> <p>Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage. </p>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)
<p><span>This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives. </span></p> <p><span>The survey targeted <span>European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens </span><span>at the local and/or regional scale.</span></span></p> <p><span><span>The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a></span></span></p>
Host-adaptation in Legionellales is 1.9 Gya, coincident with eukaryogenesis
<p>This dataset contains genomes, proteomes and protein alignments mentioned in Hugoson et al (2021). It has been used to analyze the evolution of host-adaptation in the order Legionellales.</p> <p>The data is organized by dataset type, and then by dataset.</p> <p>The four datasets used here are</p> <ul> <li><strong>Gamma105</strong>, comprising 105 <em>Gammaproteobacteria</em> and 5 outgroups;</li> <li><strong>Legio93</strong>, comprising 93 <em>Legionellales</em> and 20 outgroups;</li> <li><strong>Bacteria134</strong>, built on Gamma105, adding 27 genomes from Betts et al. (2018)</li> <li><strong>Bacteria93</strong>, built by removing <em>Legionella</em>, <em>Francisella</em>, <em>Fangia</em> and <em>Piscirickettsia</em> genera from Bacteria134</li> </ul> <p><strong>1_genomes</strong><br> Genomes as downloaded or assembled</p> <ul> <li>1_1_Gamma105</li> <li>1_2_Legio93</li> </ul> <p><strong>2_proteomes</strong><br> Proteomes, as annotated by prokka</p> <ul> <li>2_1_Gamma105</li> <li>2_2_Legio93</li> <li>2_3_Bacteria134</li> </ul> <p><strong>3_alignments</strong></p> <p>In the first three and the fifth folders, the following files are found. All sequence and alignment files are in fasta format:</p> <ul> <li>*_concatenated.fasta: concatenated alignment, trimmed.</li> <li>*.map: map of the files, tab-separated. The first row is a title row. The three first columns give the organism, the marker and the id (as found in the fasta file) for the protein.</li> <li>*_unaligned: non-aligned sequences for each marker.</li> <li>*_aligned: aligned sequences, for each marker. The prefix gives the software used for the alignment.</li> <li>*_trimmed: aligned, trimmed sequences for each marker. The prefix gives the software used to trim the alignment.</li> </ul> <p> </p> <ul> <li><strong>3_1_Gamma105</strong>: Based on the Bact109 set of markers.</li> <li><strong>3_2_Legio93</strong>: Based on the Bact109 set of markers.</li> <li><strong>3_3_Bacteria134</strong>: Based on Gamma105 set and Bact109 set of markers.</li> <li><strong>3_4_Bacteria93</strong>: Based on Bacteria134 (removed fast-evolving genomes).</li> <li><strong>3_5_TB4SS_auto</strong>: Alignment of 12 genes of the T4BSS, automatically detected in all genomes. </li> <li><strong>3_6_TB4SS_manual</strong>: Alignment of 25 genes of the T4BSS, manually curated by collinearity analysis.</li> </ul> <p> </p>
Locally adaptive temperature response of vegetative growth in Arabidopsis thaliana
<p>We investigated early vegetative growth of natural <em>Arabidopsis thaliana</em> accessions in cold, non-freezing temperatures, similar to temperatures these plants naturally encounter in fall at northern latitudes.</p> <p>Dataset includes:<br> - rosette area measurements over 3 weeks in a 16ºC and a 6ºC treatment. First phenoptying time point is at 14 days after stratification. Measurements were take twice per day.<br> These data are in file <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/rawdata_combined_annotation.txt?versionId=7b707f81-723f-4059-b72b-9dfb9f5ddd2e">rawdata_combined_annotation.txt</a> and go together with <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/outliers.csv?versionId=7287c919-1ed1-4b65-8e25-a75bb312c8fa">outliers.csv</a>, which contains outlying datapoints.</p> <p>- Seed Size measurements.<br> These data are in file <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/seed_size_swedes_lab_updated.csv?versionId=fb739477-862b-45cb-8074-7a1d8e1650bb">seed_size_swedes_lab_updated.csv </a><br> </p> <p>The remainnig files are required to rerun the analyses and recreate figures.<br> Scripts to do so can be found in https://github.com/picla/growth_16C_6C/</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/1001genomes-accessions.csv?versionId=ee605038-bd9e-448f-9c96-1a8e980c1755">1001genomes-accessions.csv</a>: lists all accession from the 1001genomes project and their respective subpopulations.</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/2029_modified_MN_SH_wc2.0_30s_bilinear.csv?versionId=73c6c2bf-97bd-425f-bf7e-14b5a7cb162f">2029_modified_MN_SH_wc2.0_30s_bilinear.csv</a>: contains climate data for each accession, downloaded and prcocessed from www.worldclim.org</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/metabolic_distance.csv?versionId=8456f998-d0dc-4a80-b96f-c0c66c1c9731">metabolic_distance.csv</a>: contains the metabolic distance as calculated in Weiszmann et al. (https://www.biorxiv.org/content/10.1101/2020.09.24.311092v1)</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/RNAseq_samples.txt?versionId=6ae1518b-1a70-440d-b0bd-0ccdcb66665e">RNAseq_samples.txt</a>: sample description of the RNA-seq samples (data is downloadable from <a href="http://www.ncbi.nlm.nih.gov/bioproject/807069">http://www.ncbi.nlm.nih.gov/bioproject/807069)</a></p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/ZAT12_downregulated_table10.csv?versionId=c6f7aa54-cb07-4378-a5a0-de12c6979b9b">ZAT12_downregulated_table10.csv</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/ZAT12_upregulated_table9.csv?versionId=911a2aa2-f08f-4a20-85de-cfa7c58b73a8">ZAT12_upregulated_table9.csv</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF_regulon_DOWN_ParkEtAl2015.txt">CBF_regulon_DOWN_ParkEtAl2015.txt</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF_regulon_UP_ParkEtAl2015.txt?versionId=f7cacbda-eea6-4ac9-8f71-5ba74e3a67c4">CBF_regulon_UP_ParkEtAl2015.txt, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF2_downregulated_table8.csv">CBF2_downregulated_table8.csv, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF2_upregulated_table7.csv">CBF2_upregulated_table7.csv, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/HSFC1_regulon_ParkEtAl2015.txt">HSFC1_regulon_ParkEtAl2015.txt</a>: these files list genes that are involve din cold acclimation as described by Park et al. (https://onlinelibrary.wiley.com/doi/10.1111/tpj.12796), and Vogel et al.(https://onlinelibrary.wiley.com/doi/10.1111/j.1365-313X.2004.02288.x).</p> <p><strong>Material and Methods</strong></p> <p><em><strong>Rosette growth</strong></em></p> <p>Seeds of 249 natural accessions (Suppl. Data 1) of <em>Arabidopsis thaliana</em> described in the 1001 genomes project <a href="https://paperpile.com/c/UDgV3V/DUBI">(1001 Genomes Consortium 2016)</a> were sown on sieved (6 mm) substrate (Einheitserde ED63). Pots were filled with 71.5 g ±1.5 g of soil to assure homogenous packing. The prepared pots were all covered with blue mats <a href="https://paperpile.com/c/UDgV3V/1WUv">(Junker et al. 2014)</a> to enable a robust performance of the high-throughput image analysis algorithm. Seeds were stratified (4 days at 4ºC in darkness) after which they germinated and left to grow for 2 weeks at 21ºC (relative humidity: 55 %; light intensity: 160 µmol m-2 s-1; 14 h light). The temperature treatments were started by transferring the seedlings to either 6 °C or 16 °C. To simulate natural conditions temperatures fluctuated diurnally between 16-21 °C, 0.5-6 °C and 8-16 °C for the 21 °C initial growth conditions and the 6 °C and 16 °C treatments, respectively (<a href="https://docs.google.com/document/d/1Bmr7p24ZMh4yPFVV5oPeH2-T5S41TOFDS3au8JhtwsU/edit#fig_design">Fig.2</a>). Light intensity was kept constant at 160 µmol m-2 s-1 throughout the experiment. Relative humidity was set at 55% but in colder temperatures it rose uncontrollably to maximum 95%. Daylength was 9h during the 16°C and 6°C treatments.</p> <p>Each temperature treatment was repeated in three independent experiments. Five replicate plants were grown for every genotype per experiment. Plants were randomly distributed across the growth chamber with an independent randomisation pattern for each experiment. During the temperature treatments (14 DAS – 35 DAS), plants were photographed twice a day (1 hour. after/before lights switched on/off), using an RGB camera (IDS uEye UI-548xRE-C; 5MP) mounted to a robotic arm. At 35 DAS, whole rosettes were harvested, immediately frozen in liquid nitrogen and stored at -80 °C until further analysis. Rosette areas were extracted from the plant images using Lemnatec OS (LemnaTec GmbH, Aachen, Germany) software.</p> <p><em><strong>Seed size</strong></em></p> <p>We used the seeds produced by <a href="https://paperpile.com/c/UDgV3V/Jqsd">(Kerdaffrec et al. 2016)</a> and limited our measurements to the set of 123 Swedish accessions that overlapped with our growth dataset. After seed stratification for four days at 4ºC in darkness, mother plants were grown for 8 weeks at 4ºC under long-day conditions (16h light; 8h dark) to ensure proper vernalization. Temperature was raised to 21ºC (light) and 16ºC (dark) for flowering and seed ripening. Seeds were kept in darkness at 16ºC and 30% relative humidity, from the harvest until seed size measurements. For each genotype three replicates were pooled and about 200-300 seeds were sprinkled on 12 x 12 cm square, transparent Petri dishes. Image acquisition was performed as described in <a href="https://paperpile.com/c/UDgV3V/WH1e">(Exposito-Alonso et al. 2018)</a> by scanning dishes on a cluster of eight Epson V600 scanners. The resulting 1200 dpi .tiff images were analyzed in the Fiji software. Images were converted to 8-bit binary images and thresholded with the <em>setAutoThreshold("Defaultdark”) </em>command, and seed area was measured in squared mm by running the <em>Analyse Particles</em> command (inclusion parameters: size=0.04-0.25 circularity=0.70-1.00).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)
<p>Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms.</p> <p>In this context, <a href="https://github.com/di-unipi-socc/FogMon">FogMon</a>, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead.</p> <p>The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. <a href="https://github.com/di-unipi-socc/FogMon-LiSCIo/tree/2.0">FogMon 2.0</a>, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.</p>
Crop classification dataset for testing domain adaptation or distributional shift methods
<p>In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: <a href="https://doi.org/10.1016/j.rse.2021.112488">https://doi.org/10.1016/j.rse.2021.112488</a> (arXiv version: <a href="https://arxiv.org/pdf/2109.01246.pdf">https://arxiv.org/pdf/2109.01246.pdf</a>). </p> <p>In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.</p> <p>More details on the dataset and processing steps can be found in <a href="https://doi.org/10.1016/j.rse.2021.112488">Kluger et. al. (2021)</a>. Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in <a href="https://openreview.net/forum?id=5HR3vCylqD">Yeh et. al. (2021)</a>, and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.</p> <p>All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".</p> <p><strong>Preferred Citation:</strong></p> <p>-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.</p> <p>-URL to this Zenodo post https://zenodo.org/record/6376160</p>
SMART - Self-adaptive Machine Learning Approach for Real-time Tuning of IEEE 802.11 PHY and MAC layers
<p><strong>Introduction</strong></p> <p>Worldwide the demand for wireless access networks providing very high throughputs has been increasing exponentially, namely due to bandwidth-hungry applications such as high definition video streaming and augmented reality. In order to fulfil these requirements, the Wi-Fi standard was enriched with new amendments, such as IEEE 802.11n, IEEE 802.11ac, and recently IEEE 802.11ax (Wi-Fi 6). New parameters have been proposed for both physical (PHY) and media access control (MAC) layers, including channel bonding, short guard interval (SGI), and advanced modulation and coding schemes (MCS).</p> <p>However, the high variability of the signal strength in the wireless radio channel, allied to the channel asymmetry, makes the selection of optimal configurations for these parameters a challenge. Typically, these parameters are configured with a default value. For runtime optimization, some algorithms have already been proposed. Still, they were designed considering legacy IEEE 802.11 releases and static scenarios. Besides, these parameters have their trade-offs that need to be properly managed. To help dealing with this, machine learning has been recently introduced in wireless networks, providing the intelligence that networks need in order to be smart and self-adaptive.</p> <p>SWOP (Smart Wireless Optimization) is a cross-layer optimization approach for Wi-Fi networks extending the current Rate Adaptation (RA) approach, for instance, followed by the well-known Minstrel algorithm widely used in practice. Our approach takes advantage of Deep Reinforcement Learning (DRL) in order to learn the optimal Wi-Fi link configuration. By considering the wireless channel as the environment, the transmitter node (the agent) chooses the best link parameters (the action) in order to maximize the throughput (the reward) based on the channel metrics captured from the environment (the state). In this work we propose a simple DRL-based Wi-Fi Rate Adaptation (RA) algorithm, named Data-driven Algorithm for Rate Adaptation (DARA), which is one of the modules of Smart Wireless Optimization (SWOP)</p> <p>SMART aimed to run a set of wireless experiments on top of w-iLab.t testbeds provided by the Fed4FIRE+ project to directly validate our DRL model and learn a policy from the wireless experiments executed in a controlled environment. However, after facing difficulties with the scenarios we could achieve on the real testbed, we decided to train and test DARA using a trace-based simulation approach. In simulation, we could train our model in scenarios that are more complex and diverse whilst easy to configure, when compared to real testbeds. The w-iLab.t testbeds were still used to capture data traces (e.g. Signal-to-Noise Ratio, position of nodes, transmission power and link distance) that were then injected in ns-3 for validating DARA.</p> <p>With this work, we concluded that DARA performance is impacted when operating in scenarios with asymmetric links, which is common in the highly dynamic and unpredictable wireless environments. Furthermore, the asymmetry offset varies between scenarios and it may also change for the same scenario, as time progresses. This randomness is not addressed when solely considering the SNR as the link metric, posing a challenge in the learning phase of DARA. Despite these limitations, the results obtained show that DARA still achieves up to 14.9% higher throughput higher than Minstrel [1] and slightly lower than Ideal [2] for most of the scenarios. The results obtained will serve as a basis to support our ongoing and future research.</p> <p> </p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the SMART project, organized in different folders for each Rate Adaptation Algorithm, as well as the traces that were used to obtain such results:</p> <ul> <li><strong>DARA: </strong>Results obtained using our solution <strong>(Naming Convention #1, Folder Content #1)</strong></li> <li><strong>MIN: </strong>Results obtained using Minstrel-HT <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>ID: </strong>Results obtained using Ideal <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>TRACES: </strong>Trace files used to obtain the results present in this dataset <strong>(Naming Convention #2, Folder Content #3)</strong></li> </ul> <p><strong>Naming Convention #1 – RAA TID TP TO:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> (RAA) </strong> <ul> <li><strong>drl </strong>– Data Driven Algorithm for Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> <li>Trace ID<strong> (TID)</strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transport Protocol<strong> (TP)</strong> <ul> <li><strong>udp </strong>– User Datagram Protocol</li> </ul> </li> <li>Traffic Orientation<strong> (TO)</strong> <ul> <li><strong>normal </strong>– A<strong>-></strong>B</li> <li><strong>reversed </strong>– B<strong>-></strong>A</li> </ul> </li> </ul> <p><strong>Naming Convention #2 – TID_TXP:</strong></p> <ul> <li>Trace ID<strong> (TID) </strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transmitting Power in dBm <strong>(TXP) </strong> <ul> <li><strong>3, 5, 7, 9, 12 dBm </strong></li> </ul> </li> </ul> <p><strong>Folder Content #1: </strong></p> <ul> <li><em>checkpoint_ RAA TID TP TO</em><strong> (Folder)</strong> <ul> <li><strong>Policy Checkpoint</strong> with which the results were obtained</li> </ul> </li> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Step Counter</strong></li> <li>Column 2 – <strong>Reward Value</strong></li> <li>Column 3 – <strong>Observation Value</strong></li> <li>Column 4 – <strong>Action Value</strong></li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #2: </strong></p> <ul> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #3 - </strong>Source: <a href="https://zenodo.org/record/3713271#.YjjBVDXLdhE">https://zenodo.org/record/3713271#.YjjBVDXLdhE</a><strong>: </strong></p> <p>· <em>date_time</em><strong>.cfg </strong>configuration details of the experiment</p> <p>· <em>date_time_NodeID</em><a href="https://zenodo.org/record/3713271#_ftn1"><strong><em><sup>[1]</sup></em></strong></a><em>_SenderID</em><a href="https://zenodo.org/record/3713271#_ftn2"><strong><em><sup>[2]</sup></em></strong></a><em>_ReceiverID</em><a href="https://zenodo.org/record/3713271#_ftn3"><strong><em><sup>[3]</sup></em></strong></a><em>_FlowType</em><a href="https://zenodo.org/record/3713271#_ftn4"><strong><em><sup>[4]</sup></em></strong></a><em>_Params</em><a href="https://zenodo.org/record/3713271#_ftn5"><strong><em><sup>[5]</sup></em></strong></a><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </p> <p>· <em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </p> <p><a href="https://zenodo.org/record/3713271#_ftnref1"><sub>[1]</sub></a><sub> ID of the node Logging node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref2"><sub>[2]</sub></a><sub> ID of the Sender node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref3"><sub>[3]</sub></a><sub> ID of the Receiver node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref4"><sub>[4]</sub></a><sub> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref5"><sub>[5]</sub></a><sub> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p> <p> </p> <p><strong>References</strong></p> <p>1. F. FietKau, “Minstrel_HT: New rate control module for 802.11n [LWN.net]”. Mrt-2010.</p> <p>2. “ns-3: ns3::IdealWifiManager Class Reference,” Jan 2021, [Online; accessed 23. Jun. 2021]. Available: <a href="https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi%20manager.html">https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi manager.html</a></p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Adapting the Harmonized Data Quality Framework for Ontology Quality Assessment
<p>Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.</p>
Infant N290 event-related potentials and stimulus-specific adaptation to face stimuli
<p>Data for publication: "Neural specialization to human faces at the age of 7 months".</p> <p>Further details will be available at: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>Metadata:</p> <p>I. Event-related potentials</p> <p>The current study investigated both cortical sensitivity and categorical specificity through event-related potentials (ERPs) previously implicated in face processing in 7-month-old infants (N290) and adults (N170). Using a category-specific repetition/adaptation paradigm, cortical specificity to human faces, or control stimuli (cat faces), was operationalized as changes in ERP amplitude between conditions where a face probe was alternated with categorically similar or dissimilar adaptors. In adults, increased N170 for human vs. cat faces and category-specific release from adaptation for face probes alternated with cat adaptors was found. In infants, a larger N290 was found for cat vs. human probes. Category-specific repetition effects were also found in infant N290 and the P1-N290 peak-to-peak response where latter indicated category-specific release from adaptation for human face probes resembling that found in adults.</p> <p>*Dataset files: N290_P1_infant, N170_P1_adult.sav</p> <p> </p> <p>*EEG data in EEGLAB’s format,</p> <p>Filenames indicate type of data with:</p> <p>1) Initial numerical code indicating (anonymized) participant number, "adult/infant" indicating participant group</p> <p>2) f1/c2/f3/c3 indicating stimulus conditions probe:face, adaptor:face / probe:cat, adaptor:cat / probe:face, adaptor:cat / probe:cat,adaptor:face, respectively</p> <p> 3) Final number _1_ or _2_ indicates block number for adult participants (in the order of presentation)</p> <p>Files with only codes 1) and 2) are continuous data with all triggers included</p> <p> </p> <p>*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx</p> <p> </p> <p>*Analysis syntax: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>*Dataset updates: TBA at https://github.com/yrttiahoS/ssa/</p> <p> </p> <p> </p> <p>II. Temperament questionnaire</p> <p>*Data collection: Temperament data were collected from participants of an infant event-related potential (ERP) study as potential correlate of outcome variables and as a descriptor of the participant group. Participants were 7-month-old infants from families volunteering in the brain research study, which were contacted through information from a population registry sample. Infant temperament was assessed by parent-reported IBQ-R short form (Putnam, S. P., Helbig, A., Gartstein, M.A., Rothbart, M.K. & Leerkes, E. M. (2014). <em>Development and assessment of short and very short forms of the Infant Behavior Questionnaire-Revised.</em> Journal of Personality Assessment, 96, 445-458. Finnish translation: Professor Katri Räikkönen-Talvitie and the Developmental Psychology Research Group University of Helsinki, Finland)</p> <p>*Authors of the dataset: Santeri Yrttiaho, Mikko Peltola, Anneli Kylliäinen, Tiina, Parviainen, Jari Hietanen</p> <p>*Site of data collection: Human Information Processing laboratory, Tampere University, Finland</p> <p>*Funding: Emil Aaltonen Foundation, Tampere University, Academy of Finland</p> <p>*Participant demographics: Participant group is described by age of M(SD) = 30.5(0.5) weeks. Participants were from Tampere metropolitan area, Finland. Data were collected during Fall 2020 (October 16th – December 7th, 2020).</p> <p>*Data content. Initial data will contain group level statistics and the individual data will be made available as additional files after publication of study results in a peer-reviewed article. Data is anonymized. Individual data contains IBQ-R short form items, scales, and factors as well as participant gender and age in weeks. Data also includes variable indicating whether the participant was included in the final ERP analysis after EEG quality control.</p> <p>*variable names: see 'Scale abbreviations.docx' and for full discussion the original article by Putnam et al. (2014).</p> <p>*Dataset files</p> <p>1. IBQ-R-short-Finnish_dataset.sav / data of items, scales, and factors</p> <p>2. SSA_infant_ibq-r_summary.sps / syntax for computing scores</p> <p>3. IBQ-R-short-Finnish_descriptives.spv / output of descriptive statistics</p> <p>4. Scale abbreviations.docx / explanation of variable names</p> <p> </p> <p> </p> <p> </p> <p> </p>
Supplementary run files for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins"
<p>TREC-Format run files of all trained models as supplementary material for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins".</p> <p>File naming follows the schema: <code>{model}-{loss variant}-{in-batch usage}-{dataset}.txt.gz</code></p>
Datasets of "Terrain-adaptive PCGML in Minecraft"
<p>Datasets of Minecraft world samples created and used for the paper <em>Terrain-adaptive PCGML in Minecraft</em> by Van der Staaij et al., as well as all uncompressed renders made to qualitatively evaluate trained machine learning models for said paper.</p>
Archetypes of climate change adaptation among large-scale arable farmers in southern Romania
<p>Supplementary material belonging to the publication.</p> <p>Two files:</p> <p>1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020.</p> <p>2. PDF with interview guideline</p>
Data for "Adaptive and Resilient Soft Tensegrity Robots" (Rieffel & Mouret, 2018)
<p>Data (experimental results) for the paper "Adaptive and Resilient Soft Tensegrity Robots", to appear in Soft Robotics (2018).</p> <ul> <li>Source code: <a href="https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity">https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity </a></li> <li>Pre-print: <a href="https://arxiv.org/abs/1702.03258">https://arxiv.org/abs/1702.03258</a></li> </ul>
Data from A functional transcriptomics analysis in the relict marsupial Dromiciops gliroides reveals adaptive regulation of protective functions during hibernation
<p>This dataset contains files with the differentially expressed genes, raw counts, DESeq2 analyses and assembled transcriptome of D. gliroides. This information is linked to the manuscript published in Molecular Ecology.</p>
Historical contingency shapes adaptive radiation in Antarctic fishes [Data set]
<p>Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of notothenioid fishes and outgroups. </p> <p>Published in : Daane, JM, Dornburg, A, Smits, P, MacGuigan, D, Hawkins, B, Near, TJ, Detrich, HW III*, Harris MP*. (2019). Historical contingency shapes adaptive radiation in Antarctic fishes. <em>Nature Ecology & Evolution.</em></p> <p> </p> <p>-contigs.zip contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence</p> <p>-cnes.zip contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.zip</p> <p>-exons.zip contains the targeted protein coding exons isolated from the larger contigs in contigs.zip</p> <p>-protein.zip contains the translated protein coding exons from exons.zip</p>
ScienceDex guides
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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.