Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis

Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat. Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file. Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file. Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.

opencc-by-4.0Jun 2021View details →

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs - Dataset

Dataset providing a set of measurement of performance and power consumpetion of a virtualized Base Station (srseNB).

opencc-by-4.0Oct 2022View details →

Dataset of Automatically Orchestrable GitHub Projects

This dataset accompanies the submission "Generating representative, live network traffic out of millions of code repositories" at HotNets'22: The 21st ACM Workshop on Hot Topics in Networks. Please see the files: - `list_of_github_repositories.txt` for a list of GitHub repositories that we found containing a `docker-compose*.yml` file - `list_of_executed_repositories.csv` for more detailed information on the success of capturing traffic with specific orchestration files found in ~67% of the repositories If you use our dataset, please cite our work as follows: <blockquote> Tobias Bühler, Roland Schmid, Sandro Lutz, and Laurent Vanbever. 2022. Generating representative, live network traffic out of millions of code repositories. In The 21st ACM Workshop on Hot Topics in Networks (HotNets ’22), November 14–15, 2022, Austin, TX, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/ 3563766.3564084 </blockquote>

opencc-by-4.0Jun 2024View details →

Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus: optogenetical stimulation data

This dataset contains 2-photon calcium imaging data from the paper 'Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus'. This is the calcium imaging data from CA1 pyramidal cells and interneurons, including both spontaneous activity and activity in response to optogenetic stimulation. Data organization This dataset contains all the data related to the all-optical part of the paper and was analyzed using the code from the <a href="https://gitlab.com/cossartlab/bocchio-vorobyev-et-al-2023/-/tree/main/Optogenetical%20stimulation?ref_type=heads">lab repository</a>. The original calcium imaging movies are excluded due to size limitations. Further information Please email vorobev[a t]phystech.edu if you need further information on the data or if you wish to access the raw calcium imaging movies (not uploaded here due to storage limitations).

opencc-by-4.0Oct 2019View details →

Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds

The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond.  DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland).   While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable.   In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated. This datased includes the reports provided by the UE to ONAP.   As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE.  For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO.   DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA.  DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds. This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.

opencc-by-4.0Sep 2024View details →

EDGELESS: orchestration domain with 5 VM nodes

<div> <h2>Scenario</h2> <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#scenario"></a></div> <ul> <li>ε-ORC, ε-CON, Redis in a VM</li> <li>5 nodes in other VMs: <ul> <li>4 with 2 cores</li> <li>1 with 8 cores</li> </ul> </li> </ul> Factors: <ul> <li>load, in terms of the interarrival between consecutive workflows, with the average ranging from 10 s to 60 s</li> <li>orchestration policy, i.e., either <code>Random</code> or <code>RoundRobin</code></li> </ul> Each experiment lasts 1 hour and is repeated 10 times. Workload generated with <code>edgeless_benchmark</code>, see <code>run.sh</code> for the complete set of parameters. <div> <h2>Repeatability</h2> Check GitHub repository with the EDGELESS <a href="https://github.com/edgeless-project/edgeless/">reference implementation</a> and <a href="https://github.com/edgeless-project/cnr-experiments">experiments</a> (experiment <code>003-tid-benchmark-varload</code>) <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#repeatability"></a></div> <ol> <li>Install a Redis server</li> <li>Update the ε-ORC and ε-CON configuration in <code>conf/</code></li> <li>Install a cluster of EDGELESS nodes</li> <li>Use <code>run.sh</code> to run experiments by saving the output data in <code>dataset</code></li> </ol> <h2>Dataset content</h2> The compressed archive <code>003-tid-benchmark-varload.zip</code> contains the following files, all with CSV entries: <table> <tbody><tr> <th>Filename</th> <th>Format</th> </tr> </tbody><tbody> <tr> <td><code>health_status.csv</code></td> <td>timestamp,node_id,node_health_status</td> </tr> <tr> <td><code>capabilities.csv</code></td> <td>timestamp,node_id,node_capabilities</td> </tr> <tr> <td><code>mapping_to_instance_id.csv</code></td> <td>timestamp,logical_id,node_id1,physical_id1,...</td> </tr> <tr> <td><code>performance_samples.csv</code></td> <td>metric,identifier,value,timestamp</td> </tr> <tr> <td><code>application_metrics.csv</code></td> <td>entity,identifier,value,timestamp</td> </tr> </tbody> </table> Notes: <ul> <li>The timestamp format is always A.B, where A is the Unix epoch in seconds and B is the fractional part in nanoseconds.</li> <li>All the identifiers (node_id, logical_id, and physical_id) are UUID.</li> <li>The field entity in the application metrics can be <code>f</code> (function) or <code>w</code> (workflow).</li> </ul>

Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 4 relative to Figure 7 – ArhGEF11 CRISPR interference

Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to Figure 7B and Figure 7 - Figure Supplement 6 (see Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells). The source data comprises for each 48 - 55 hpf Tg(kdrl:eGFP-JAM3b; kdrl:nls-mKate2) zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 2 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.

Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 3 relative to Figure 7 – ArhGEF11 morpholino splicing interference

Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to Figure 7A and Figure 7 - Figure Supplement 5 (see Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells). The source data comprises for each 48 - 55 hpf Tg(kdrl:eGFP-JAM2a; kdrl:nls-mKate2) zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 3 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.

Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 1 relative to Figure 3

Raw image files (TIFF format) relative to Figure 3 (see Materials and Methods — Dt-runx1 phenotype analysis – cell count). The source data comprises for each 52 - 55 hpf zebrafish embryo 3 z-stack (segments 1 to 3) encompassing the whole length of the aorta, for control condition (Tg(Kdrl:Gal4;UAS:RFP), n = 3 individuals) and mutant condition (Tg(kdrl:Gal4;UAS:RFP;4xNR:dt-runx1-eGFP), n = 7 individuals). For control condition, one fluorescence channel was acquired, corresponding to the cytoplasmic RFP expressed in endothelial cells. For mutant condition, two fluorescence channels were acquired, corresponding first to the cytoplasmic RFP expressed in endothelial cells using the same reporter as for the control condition, and second the cleaved cytoplasmic GFP reporting the expression of our dt-runx1 mutant construct in endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm.

Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 2 relative to Figure 4

Raw image files (TIFF format) and segmented 3D images (.ims, Imaris proprietary files) relative to Figure 4 and Figure 4 Figure Supplement 3 (see Materials and Methods — RNAscope image analysis – Pard3). The source data comprises for each 52 - 55 hpf zebrafish embryos 2 z-stack (segments 1 to 2) encompassing the whole length of the aorta, for control condition (Tg(Kdrl:eGFP), n = 7 individuals) and mutant condition (Tg(kdrl:Gal4; 4xNR:dt-runx1-eGFP), n = 12 individuals). For both control and mutant conditions, two fluorescence channels are displayed, corresponding to the cytoplasmic GFP expressed in endothelial cells (in green) and the RNAscope signal (OPAL-570, in magenta). Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.4 µm. The .ims files contain the 3D rendering of the z-stacks as well as the segmentations of Pard3ba mRNA RNAscope spots (in magenta), in the aorta (Spots 1 Selection) or outside (Spots 1), as well as the segmentation of endothelial cells (green) (Cells 1) and hemogenic endothelial cells (Cells 1 Cell export).

opencc-by-4.0Aug 2017View details →

PHENICX-Anechoic: note annotations for Aalto anechoic orchestral database

PHENICX-Anechoic: denoised recordings and note annotations for Aalto anechoic orchestral database       Description   This dataset includes audio and annotations useful for tasks as score-informed source separation, score following, multi-pitch estimation, transcription or instrument detection, in the context of symphonic music.   This dataset was presented and used in the evaluation of:   M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016))"   On this web page we do not provide the original audio files, which can be found at the web page hosted by Aalto University. However, with their permission we distribute the denoised versions for some of the anechoic orchestral recordings:   Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008.   For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the web page and the associated publication [2]   We provide the associated musical note onset and offset annotations, and the Roomsim[3] configuration files used to generate the multi-microphone recordings [1].   The anechoic dataset in [2] consists of four passages of symphonic music from the Classical and Romantic periods. This work presented a set of anechoic recordings for each of the instruments, which were then synchronized between them so that they could later be combined to a mix of the orchestra. In order to keep the evaluation setup consistent between the four pieces, we selected the following instruments: violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet and bassoon.   We created a ground truth score, by hand annotating the notes played by the instruments. The annotation process involved gathering the original scores in MIDI format, performing an initial automatic audio-to-score alignment, then manually aligning each instrument track separately with the guidance of a monophonic pitch estimation.   During the recording process detailed in [2], the gain of the microphone amplifiers was fixed to the same value for the whole process, which reduced the dynamic range of the recordings of the quieter instruments. This lead to problems with which we had to deal, in order to reduce the noise. In the paper we described the score-informed denoising procedure we applied to each track.   A complete description of the dataset and the creation methodology, including the generation of the multi-microphone recordings, is presented in [1].     Files included The “audio” folder contains the audio files for each instrument in a given source: sourcenumber.wav, where “source” can be either violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet or bassoon and “number” corresponds to the each separated instrument in a given source (e.g. there are two violins in the “mozart” piece, thus you will find “violin1.wav” and “violin2.wav” in the “mozart” folder).   The “annotations” folder includes note onsets and offset annotations as MIDI and text files for the corresponding audio files in the dataset. The annotations are offered per source: source.txt and source.mid, where “source” can be either violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet or bassoon. Additionally, for tasks as score-following, we provide MIDI which is not aligned with the audio as MIDI and text file: source_o.txt and source_o.mid. Furthermore, an additional MIDI file all.mid holds the tracks for all the sources in a single MIDI file. The text files comprise all the notes played by a source in the following format: Onset,Offset,Note name We recommend using the ground truth annotations from the text file as the MIDI might have problems due to the incorrect duration for some notes.   The “Roomsim” folder contains the configuration files (“Text_setups”) and the impulse responses (“IRs”) which can be used with Roomsim[2] to generate the corresponding room configuration and the multi-microphone audio tracks used in our research. In the “Text_setups” folder, one can find the Roomsim text setups for the microphones: C,HRN,L,R,V1,V2,VL,WW_L,WW_R,TR. The “IRs” folder contains two subfolders: “conf1” can be used to generate the recordings for the Mozart piece, and “conf2” for the Bruckner, Beethoven, and Mahler pieces. We provide IR “.mat” files for each of the pairs (“microphone”,”source”): microphone_Ssourcenumber.mat, where “microphone” is C,HRN,L,R,V1,V2,VL,WW_L,WW_R,TR, and “sourcenumber” is the number of the sources ordered as in this list: bassoon (1), cello(2), clarinet(3), double bass(4), flute(5), horn(6), viola(7), violin(8), oboe(9), trumpet(10). Please consider that the Mozart piece does not contain oboe nor trumpet.     Conditions of Use The annotations and the Roomsim configuration files in the PHENICX-Anechoic dataset are offered free of charge for non-commercial use only. You can not redistribute them nor modify them. Dataset by Marius Miron, Julio Carabias-Orti, Juan Jose Bosch, Emilia Gómez and Jordi Janer, Music Technology Group - Universitat Pompeu Fabra (Barcelona). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the web page and the associated publication [2].   Please Acknowledge PHENICX-Anechoic in Academic Research When the present dataset is used for academic research, we would highly appreciate if scientific publications of works partly based on the PHENICX-Anechoic dataset quote the following publications:   M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016)   Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008.   Download Dataset available Go to our download page.   Feedback Problems, positive feedback, negative feedback, help to improve the annotations... it is all welcome! Send your feedback to: marius.miron@upf.edu AND mtg@upf.edu In case of a problem report please include as many details as possible.   References [1] M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016) [2] Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008. [2] Campbell, D., K. Palomaki, and G. Brown. "A Matlab simulation of" shoebox" room acoustics for use in research and teaching." Computing and Information Systems 9.3 (2005): 48.

opencc-by-4.0Nov 2018View details →

Structured feature comparison between container orchestration frameworks

Container orchestration frameworks provide support for management of complex distributed applications. Different frameworks have emerged only recently, and they are in constant evolution as new features are being introduced. This reality makes it difficult for practitioners and researchers to maintain a clear view on the technology space, and hinders selecting the most appropriate framework, assessing the maturity and the lock-in risks for each framework. We present an descriptive feature comparison study of the five most prominent orchestration solutions: Docker Swarm, Kubernetes, and Mesos, combined with Marathon or Aurora, and DC/OS. This study aims at (i) identifying the common and unique features of all frameworks, (ii) comparing these frameworks qualitatively ánd quantitatively with respect to: (ii.a) genericity in terms of supported features and (ii.b) vendor lock-in, (iii) investigating the maturity and stability of the frameworks as well as the pioneering nature of each framework by studying the historical evolution of the frameworks on GitHub The study methodology involves feature variability and commonality analysis, mapping features to common use cases and development history on GitHub, and card sorting for defining a taxonomy of features which is divided into 9 functional aspects and 27 sub-aspects. The result is a comprehensive feature-based overview of the current state of container orchestration frameworks. The published data set contains the data used for answering all research questions (i), (ii) and (iii). This data has been processed both qualitatively as quantitatively. The scientific approach and reproducible methodology allows for continued assessment of container orchestration frameworks. The document format is Microsoft Office Word and is associated with an extended Office template that contains macro's for processing these links automatically (checkLinks creates a simple bibliography, openLinks opens all references in a selected range, deleteLinks delete Links). To activate these Macro's, save the file Normal.dotm in your local user directory that by default contains all Microsoft templates for your user account. For example in Windows, this directory is located in the c:\users\<user name>\AppData\Roaming\Microsoft\Templates

opencc-by-nc-sa-4.0Oct 2019View details →

A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music

Dataset accompanying the paper published at ISMIR 2019. See included README file for details.

Huntingtin structure is orchestrated by HAP40 and shows a polyglutamine expansion-specific interaction with exon 1

Supplementary Data files to accompany manuscript by Harding et al "Huntingtin structure is orchestrated by HAP40 and shows a polyglutamine expansion-specific interaction with exon 1" <ul> <li>Supplementary Data 1 - Multiple sequence alignment for HTT used for Consurf analysis</li> <li>Supplementary Data 2 - Multiple sequence alignment for HAP40 used for Consurf analysis</li> <li>Supplementary Data 3 - Apo HTT cryo-EM map </li> <li>Supplementary Data 4 - HTT-HAP40 Q23 regularised SAXS profile</li> <li>Supplementary Data 5 - HTT-HAP40 Q54 regularised SAXS profile</li> <li>Supplementary Data 6 - HTT-HAP40 Δexon 1 regularised SAXS profile</li> <li>Supplementary Data 7 - XL-MS data</li> <li>Supplementary Data 8 - HTT-HAP40 ensemble weightings</li> <li>Supplementary Data 9 - HTT-HAP40 Q23 ensemble models</li> <li>Supplementary Data 10 - HTT-HAP40 Q54 ensemble models</li> <li>Supplementary Data 11 - HTT-HAP40 Δexon 1 ensemble models</li> </ul>

Assessing Orchestration Load in Teacher-Facing Dashboards - Figure 2

Figure of the results of the second session in the paper Assessing Orchestration Load in Teacher-Facing Dashboards.

Assessing Orchestration Load in Teacher-Facing Dashboards - Figure 1

Figure of the results of the first session in the paper Assessing Orchestration Load in Teacher-Facing Dashboards.

Assessing Orchestration Load in Teacher-Facing Dashboards - Figure 4

Figure of the results of the fourth session in the paper Assessing Orchestration Load in Teacher-Facing Dashboards.

Assessing Orchestration Load in Teacher-Facing Dashboards - Figure 3

Figure of the results of the third session in the paper Assessing Orchestration Load in Teacher-Facing Dashboards.