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opencc-by-4.0Mar 2018View details →

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

INTRODUCTION The goal of these LPWAN datasets is to provide the global research community with a benchmark tool to evaluate fingerprint localization algorithms in large outdoor environments with various properties. An identical collection methodology was used for all datasets: during a period of three months, numerous devices containing a GPS receiver periodically obtained new location data, which was sent to a local data server via a Sigfox or LoRaWAN message. Together with network information such as the receiving time of the message, base station IDs' of all receiving base stations and the Received Signal Strength Indicator (RSSI) per base station, this location data was stored in one of the three LPWAN datasets:  <ul> <li> lorawan_dataset_antwerp.csv </li> <li> 130 430 LoRaWAN messages, obtained in the city center of Antwerp </li> <li> sigfox_dataset_antwerp.csv </li> <li> 14 378 Sigfox messages, obtained in the city center of Antwerp </li> <li> sigfox_dataset_rural.csv </li> <li> 25 638 Sigfox messages, obtained in a rural area between Antwerp and Ghent </li> </ul> As the rural and urban Sigfox datasets were recorded in adjacent areas, many base stations that are located at the border of these areas can be found in both datasets. However, they do not necessarily share the same identifier: e.g. ‘BS 1’ in the urban Sigfox dataset could be the same base station as ‘BS 36’ in the rural Sigfox dataset. If the user intends to combine both Sigfox datasets, the mapping of the ID's of these base stations can be found in the file: <ul> <li> sigfox_bs_mapping.csv </li> </ul> The collection methodology of the datasets, and the first results of a basic fingerprinting implementation are documented in the following journal paper:  <a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a>   UPDATES IN VERSION 1.3 We added the file lorawan_antwerp_gateway_locations.json.txt. As the filename suggests, this file contains the coordinates of the LoRaWAN gateways in Antwerp. (The .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo).   UPDATES IN VERSION 1.2 In this version of the LPWAN dataset, only the LoRaWAN set has been updated. The Sigfox datasets remain identical to version 1.0 and 1.1. The main updates in the LoRaWAN set are the following: <ul> <li> New data: the LoRaWAN messages in the new set are collected 1 year after the previous dataset version. To be consistent with the previous versions, the new LoRaWAN set is uploaded in the same .CSV format as before. This upload can still be found in this repository as ‘lorawan_dataset_antwerp.csv’. </li> <li> More gateways:  Compared to the previous dataset, 4 gateways were added to the LoRaWAN network. The RSSI of these gateways are shown in columns ‘BS 69’, ‘BS 70’,‘BS 71’ and ‘BS 72’. All other ‘BS’ columns are in the same order as in previous dataset versions. </li> <li> More metadata: In the previous LoRaWAN dataset, metadata was limited to 3 receiving gateways per message. In the new dataset version, metadata from all receiving gateways is included in every message. Moreover, some gateways provide a timestamp with nanosecond precision, which can be used to evaluate Time Difference of Arrival localization methods with LoRaWAN. </li> <li> 2 file formats: As more metadata becomes available, we find it important to share the dataset in a clearer overview.  This also allows researchers to evaluate the performance of LoRaWAN in an urban environment. Therefore, we publish the new LoRaWAN dataset as a .CSV file as described above, but also as a .JSON file (lorawan_antwerp_2019_dataset.json.txt, the .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo) An example of one message in this JSON format can be seen below: <ul> <li> JSON format description: <ul> <li> HDOP: Horizontal Dilution of Precision </li> <li> dev_addr: LoRaWAN device address </li> <li> dev_eui: LoRaWAN device EUI </li> <li> sf: Spreading factor </li> <li> channel: TX channel (EU region) </li> <li> payload: application payload </li> <li> adr: Adaptive Data Rate (1 = enabled, 0= disabled) </li> <li> counter: device uplink message counter </li> <li> latitude: Groundtruth TX location latitude </li> <li> longitude: Groundtruth TX location longitude </li> <li> airtime: signal airtime (seconds) </li> <li> gateways: <ul> <li> rssi: Received Signal Strength </li> <li> esp: Estimated Signal Power </li> <li> snr: Signal-to-Noise Ratio </li> <li> ts_type: Timestamp type. If this says "GPS_RADIO", a nanosecond precise timestamp is available </li> <li> time: time of arrival at the gateway </li> <li> id: gateway ID </li> </ul> </li> </ul> </li> <li> JSON example <pre><code class="language-json">{ "hdop": 0.7, "dev_addr": "07000EFE", "payload": "008d000392d54c4284d18c403333333f04682aa9410500e8fd4106cabdbc420f00db0d470ce32ac93f0d582be93f0bfa3f8d3f", "adr": 1, "latitude": 51.20856475830078, "counter": 31952, "longitude": 4.400575637817383, "airtime": 0.112896, "gateways": [ { "rssi": -115, "esp": -115.832695, "snr": 6.75, "rx_time": { "ts_type": "None", "time": "2019-01-04T08:59:53.079+01:00" }, "id": "08060716" }, { "rssi": -116, "esp": -125.51497, "snr": -9.0, "rx_time": { "ts_type": "GPS_RADIO", "time": "2019-01-04T08:59:53.962029179+01:00" }, "id": "FF0178DF" } ], "dev_eui": "3432333853376B18", "sf": 7, "channel": 8 }</code></pre>   </li> </ul> </li> </ul>

openeupl-1.2Apr 2020View details →

Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints

1. Data set for  for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures  (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD) mdifp_validation_data.tar.gz - archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in  D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. 153, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a> (2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a>   2YKI - protein-ligand complex , PDB ID 2YKI    - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)    -  ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/) 6EI5 - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5    - ref-min.pdb  minimized structure    - ref.prmtop topology file    - moe.mol2 - ligand structure in mol2 format    - amber2namd2.dcd generated MD trajectory  SAD_3-RAMD-03-2020.pkl - a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036) HSP90_Gromacs.zip - an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd) The rest of the files contains data obtained from simulation of the complex of GPCR muscarinic receptor M2 (PDB ID:4MQT); immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE)  with a small molecule agonist iperoxo.     - IXO.pdb and moe.mol2 - PDBand MOL2 structure of iperoxo    - AMBER_eq.tar.gz - structure of the equilibrated complex generated using AMBER software    - NAMD_eq.tar.gz - two equilibration trajectories in dcd format generated using NAMD software     - RAMD_eq.tar.gz - dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available)  ( *csv files were added erroneously and do not belong to the project)

opencc-by-4.0Sep 2020View details →

Averaged Spectra - Collagen fingerprinting traces the introduction of caprines to island eastern Africa

Averaged ZooMS spectra for publication on tracing the spread of caprines in island eastern Africa. Spectra also smoothed and baseline corrected.

dryad32/100

Data from: Egg morphology fails to identify nests parasitized by conspecifics in common pochard: a test based on protein fingerprinting and including female relatedness

Conspecific brood parasites lay eggs in nests of other females of the same species. A variety of methods have been developed and used to detect conspecific brood parasitism (CBP). Traditional methods may be inaccurate in detecting CBP and in revealing its true frequency. On the other hand more accurate molecular methods are expensive and time consuming. Eadie developed a method for revealing CBP based on differences in egg morphology. That method is based on Euclidean distances calculated for pairs of eggs within a clutch using standardized egg measurements (length, width and weight). We tested the applicability of this method in the common pochard Aythya ferina using nests that were identified as parasitized (39 nests) or non-parasitized (16 nests) based on protein fingerprinting of eggs. We also analyzed whether we can distinguish between parasitic and host eggs in the nest. We found that variation in MED can be explained by parasitism but there was a huge overlap in MED between parasitized and non-parasitized nests. MED also increased with clutch size. Using discriminant function analysis (DFA) we found that only 76.4% of nests were correctly assigned as parasitized or nonparasitized and only 68.3% of eggs as parasitic or host eggs. Moreover we found that MED in parasitized nests increased with relatedness of the females that laid eggs in the nest. This finding was supported by positive correlation between MED and estimated relatedness in female–female pairs. Although variation in egg morphology is associated with CBP, it does not provide a reliable clue for distinguishing parasitized nests from non-parasitized nests in common pochard.

opencc-zeroDec 2015View details →

opencc-zeroOct 2015View details →

Protein stable isotope fingerprinting (P-SIF): Pure culture samples, under various growth substrates

Carbon stable isotope data for proteins extracted from a variety of pure culture samples.

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Upright Mode

Simulation files (Gromacs 4.6.7 format) for the "E-field weak, upright mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc. Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Parallel Mode

Simulation files (Gromacs 4.6.7 format) for the "E-field weak, parallel mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc. Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Crystallographic Mode

Simulation files (Gromacs 4.6.7 format) for the "E-field weak, crystallographic mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc. Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Unbound Simulations, Set 2

Simulation files (Gromacs 4.6.7 format) for the "Unbound" simulations in Ref. [1]. There are two replicas marked with "_4" and "_5". Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Unbound Simulations, Set 1

Simulation files (Gromacs 4.6.7 format) for the "Unbound" simulations in Ref. [1]. There are three replicas marked with "_1" , "_2", and "_3". Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Clustering Simulations

Simulation files (Gromacs 4.6.7 format) for the "Clustering" simulations in Ref. [1]. There are two replicas marked with "_1" and "_2". Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Strong E-field Simulations

Simulation files (Gromacs 4.6.7 format) for the "E-field strong" simulations in Ref. [1]. There are four different systems ("Crystallographic A-form", "Crystallographic B-form", "Parallel", "Upright") with 20 replica simulations in each (marked with "_1" etc.). Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Seeding Simulations

Simulation files (Gromacs 4.6.7 format) for the "Seeding" simulations in Ref. [1]. Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

Atomistic Fingerprint of Hyaluronan-CD44 Binding: Gathering Simulations

Simulation files (Gromacs 4.6.7 format) for the "Gathering" simulations in Ref. [1]. Files include: -trajectories (.xtc) that are saved every 100ps -initial structures (.gro), -run input files (.tpr) -checkpoint files (.cpt) -simulation parameter files (.mdp) -system topology file (.top) -topology files included in the system topology file (.itp) [1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)

opencc-by-4.0Nov 2023View details →

Chronological Molecular Fingerprint of Wetland Soil by Sensitivity-Enhanced Solid-State NMR

The unprocessed ssNMR data files generated in this study "Chronological Molecular Fingerprint of Wetland Soil by Sensitivity-Enhanced Solid-State NMR".

Figs. 59–71 in Indoor Radio Map localization WiFi fingerprint datasets

Figs. 59–71. Habitus images, SEMs, habitat, and genitalia illustrations of Selenophorus species. 59–63) S. striatopunctatus dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 64–68) S. elytrostictus, new species, dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 69) Laguna Atascosa NWR, loma-freshwater margin; 70–71) S. elytrostictus, new species, male median lobe left lateral and dorsal views.

Figs. 72–75 in Indoor Radio Map localization WiFi fingerprint datasets

Figs. 72–75. Habitus images of Selenophorus and related genera, dorsal aspect: 72) S. maritimus; 73) Athrostictus punctatulus; 74) Discoderus discoderoides. 75) Sabal Palm Sanctuary, old-growth "core".

Figs. 44–50 in Indoor Radio Map localization WiFi fingerprint datasets

Figs. 44–50. Habitus images of Selenophorus species, dorsal aspect. 44) S. blanchardi; 45) S. pedicularius; 46) S. planipennis; 47) S. aeneopiceus; 48) S. breviusculus; 49) S. fatuus; 50) S. parumpunctatus.

Figs. 37–43 in Indoor Radio Map localization WiFi fingerprint datasets

Figs. 37–43. Habitus images and genitalia illustrations of Selenophorus species. 37) S. aequinoctialis, dorsal aspect; 38) S. palliatus, dorsal aspect; 39) S. sinuaticollis, dorsal aspect; 40–43) S. rileyi, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.

Figs. 29–36 in Indoor Radio Map localization WiFi fingerprint datasets

Figs. 29–36. Habitus images and genitalia illustrations of Selenophorus species. 29) S. chaparralus, dorsal aspect; 30) S. opalinus, dorsal aspect; 31) S. fabricii, dorsal aspect; 32) S. trepidus, dorsal aspect; 33–36) S. undatus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.