Find research datasets worth reusing

opencc-byNov 2017View details →

Dings - Channeled hypocaust

Channelled hypocaust and furnace http://cotswoldarchaeology.co.uk/roman-villa-at-stoke-gifford-south-gloucestershire/ Source: Objaverse 1.0 / Sketchfab

opencc-zeroJan 2024View details →

Data from: LHFPL5 is a key element to the gating spring of cochlear hair cells: comparison of mechanotransduction channel activation in presence or absence of LHFPL5

During auditory transduction, sound-evoked vibrations of the hair cell stereociliary bundles open mechanotransducer (MET) ion channels via tip links extending from one stereocilium to its neighbor. How tension in the tip link is delivered to the channel is not fully understood. The MET channel comprises a pore-forming subunit, transmembrane channel-like protein (TMC1 or TMC2), aided by several accessory proteins, including LHFPL5 (lipoma HMGIC fusion partner-like 5). We investigated the role of LHFPL5 in transduction by comparing MET channel activation in outer hair cells of Lhfpl5-/- knockout mice with those in Lhfpl5+/- heterozygotes. The 10-90 percent working range of transduction in Tmc1+/+; Lhfpl5+/- was 52 nm, from which the single-channel gating force, Z was evaluated as 0.34 pN. However, in Tmc1+/+; Lhfpl5‑/- mice, the working range increased to 123 nm and Z more than halved to 0.13 pN, indicating reduced sensitivity. Tip link tension is thought to activate the channel via a gating spring, whose stiffness is inferred from the stiffness change on tip link destruction. The gating stiffness was ~40 percent of the total bundle stiffness in wild-type but was virtually abolished in Lhfpl5-/-, implicating LHFPL5 as a principal component of the gating spring. The mutation Tmc1 p.D569N reduced the LHFPL5 immunolabeling in the stereocilia and like Lhfpl5-/- doubled the MET working range but other deafness mutations had no effect on the dynamic range. We conclude that tip-link tension is transmitted to the channel primarily via LHFPL5; residual activation without LHFPL5 may occur by direct interaction between PCDH15 and TMC1.

opencc-zeroJan 2024View details →

The potassium channel subunit Kv1.8 (Kcna10) is essential for the distinctive outwardly rectifying conductances of type I and II vestibular hair cells

In amniotes, head motions and tilt are detected by two types of vestibular hair cells (HCs) with strikingly different morphology and physiology. Mature type I HCs express a large and very unusual potassium conductance, gK,L, which activates negative to resting potential, confers very negative resting potentials and low input resistances, and enhances an unusual non-quantal transmission from type I cells onto their calyceal afferent terminals. Following clues pointing to KV1.8 (KCNA10) in the Shaker K channel family as a candidate gK,L subunit, we compared whole-cell voltage-dependent currents from utricular hair cells of KV1.8-null mice and littermate controls. We found that KV1.8 is necessary not just for gK,L but also for fast-inactivating and delayed rectifier currents in type II HCs, which activate positive to resting potential. The distinct properties of the three KV1.8-dependent conductances may reflect different mixing with other KV1 subunits, such as KV1.4 (KCNA4). In KV1.8-null HCs of both types, residual outwardly rectifying conductances include KV7 (KCNQ) channels. Current clamp records show that in both HC types, KV1.8-dependent conductances increase the speed and damping of voltage responses. Features that speed up vestibular receptor potentials and non-quantal afferent transmission may have helped stabilize locomotion as tetrapods moved from water to land.

opencc-zeroJan 2024View details →

Ecologically mediated differences in electric organ discharge drive evolution in a sodium channel gene in South American electric fishes

Active electroreception — the ability to detect objects and communicate with conspecifics via the detection and generation of electric organ discharges (EODs) — has evolved convergently in several fish lineages. South American electric fishes (Gymnotiformes) are a highly species-rich group, possibly in part due to evolution of an electric organ (EO) that produces diverse EODs. Neofunctionalization of a voltage-gated sodium channel accompanied the evolution of electrogenic tissue from muscle and resulted in a novel gene (scn4aa) uniquely expressed in the EO. Here, we investigate the link between variation in scn4aa and differences in EOD waveform. We combine gymnotiform scn4aa sequences encoding the C-terminus of the Nav1.4a protein with biogeographic data and EOD recordings. We test whether physiological transitions among EOD types accompany differential selection pressures on scn4aa. We found positive selection on scn4aa coincided with shifts in EOD types. Species that evolved in the absence of predators, which likely selected for reduced EOD complexity, exhibited increased scn4aa evolutionary rates. We model mutations in the protein that may underlie changes in protein function and discuss our findings in the context of gymnotiform signalling ecology. Together, this work sheds light on the selective forces underpinning major evolutionary transitions in electric signal production.

opencc-zeroFeb 2024View details →

Touch sensation requires the mechanically-gated ion channel Elkin1

The extraordinary speed of touch perception is enabled by mechanically-activated ion channels, the opening of which excites cutaneous sensory endings to initiate sensation. We identify Elkin1(1) as an ion channel likely gated by mechanical force necessary for normal behavioral touch sensitivity in mice. Touch insensitivity in Elkin1-/- mice was caused by a loss of mechanically-activated currents (MA-currents) in around half of all sensory neurons that are activated by light touch (low threshold mechanoreceptors, LTMRs). Reintroduction of Elkin1 into sensory neurons from Elkin1-/- mice acutely restored MA-currents. Additionally, siRNA mediated knockdown of Elkin1 from induced human sensory neurons substantially reduced indentation-induced MA-currents supporting a conserved role for Elkin1 in human touch. Our data identify Elkin1 as a novel core component of touch transduction in mammals.

opencc-zeroMar 2024View details →

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

Data for channel width of the Cannon River, Minnesota, 1938-2017

Datasets analyzed and generated to measure channel width change of the Cannon and Straight Rivers, Minnesota. Includes aerial imagery and derived files to describe river geometry. Aerial imagery was originally accessed from USGS Earth Explorer and the University of Minnesota Minnesota Historical Aerial Photographs Online portal. 1939_ASCS.zip, 1951_USDA.zip, 1948_USGS.zip, 1964_ASCS.zip, 1974_USGS_mosaic.zip, 1974_single_images.zip, 1980_USGS.zip, 2002_NAIP.zip, 2010_NAIP.zip, 2017_NAIP.zip These zip files contain aerial images from from 1938, 1951, 1958, 1964, 1974, 1980, 1991, 2002, 2010, and 2017, sorted into folders by year. Aerial images are included as raw downloaded images and (where applicable) georectified images. Ground control points used for rectification are included for rectified photos. For a number of years, a mosaicked image was created and is included. shapefiles.zip This zip file contains derived shapefiles to describe river geometry. The active channel of the river was digitized for each year of available imagery. This included the upper Cannon River (upstream of its confluence with the Straight River), the Straight River, and the lower Cannon (downstream of the confluence with the Straight River).  From the active channel boundaries, the centerline and width was extracted using the Planform Statistics tool in Arcmap (Lauer, W. NCED Stream Restoration Toolbox-Channel Planform Statistics And ArcMap Project, National Center for Earth-Surface Dynamics). Width was extracted at 10 m intervals, and the data is stored in points at each 10 m interval. Meander migration between years was also calculated using the same toolbox.  Sinuosity was calculated for channel segments of various lengths.

Artifacts for "Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls"

List of keywords used to build the text classifier for the paper Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls. For more information on how these keywords were obtained, see the "Data Labeling" section of this paper. The provided CSV file contains 3 columns: <ul> <li>"keyword": Lowercased keyword in either English, Russian, Ukranian or Polish.</li> <li>"weight": Score indicating the keyword's war-related affinity, with 3 being the most related. Negative weights are used to correct for exceptions in our classifier.</li> <li>"topic": Subject of the keyword used for topic analysis.</li> </ul>

Beta emission channeling patterns from 75Ge and 75Ga in diamond

Manuscript on "Structural formation yield of GeV centers from implanted Ge in diamond" 2-dimensional experimental beta emission channeling patterns from beta-decay of 75 Ge following 75Ga implanted in diamond as shown in Figures 2,3,4,8 of the mentioned manuscript 20x20 matrix of counts within position-sensitive detector, pixel size 1.3x1.3 mm2 at 60 cm distance from sample For <211>, <100> and <111> patterns, major horizontal planes are (110), while for the <110> patterns horizontal plane is (100) and vertical (110) Ti = implantation temperature, Ta = annealing temperature, room temperature RT=30°C    Figure 2: 75Ge patterns from "higher fluence" sample (~2E13 cm-2 per implantation step): (a) vat4579a.csv: Ti=RT <110> (b) vat4577a.csv: Ti=RT <211> (c) vat4576a.csv: Ti=RT <100> (d) vat4578a.csv: Ti=RT <111>   Figure 3: 75Ge patterns from "higher fluence" sample (~2E13 cm-2 per implantation step): (a) vat4600a.csv: Ti=RT Ta=900°C <110> (b) vat4598a.csv: Ti=RT Ta=900°C <211> (c) vat4597a.csv: Ti=RT Ta=900°C <100> (d) vat4599a.csv: Ti=RT Ta=900°C <111>   Figure 4: 75Ge patterns from "lower fluence" sample (~2E12 cm-2 per implantation step): (a) vat4679a.csv: Ti=900°C <110> (b) vat4677a.csv: Ti=900°C <211> (c) vat4676a.csv: Ti=900°C <100> (d) vat4678a.csv: Ti=900°C <111>   Figure 8: 75Ga patterns (measured during implantation) (a) vat4660a.csv: Ti=30°C <100> "lower fluence" sample (b) vat4581a.csv: Ti=30°C <100> "higher fluence" sample (c) vat4601a.csv: Ti=300°C <100> "higher fluence" sample (d) vat4617a.csv: Ti=600°C <100> "higher fluence" sample

opencc-by-4.0Apr 2024View details →

Simulated Channel state information for In-factory Subnetworks

In the simulated factory environment, a multitude of strategically deployed short-range cells forms the backbone of robotic systems, production modules, conveyors, and other industrial machinery. These cells, designated as In-factory subnetworks (InF-S), are comprised of an access point (AP) serving one or more devices within the subnetwork.

opencc-by-4.0Apr 2024View details →

Dataset for "Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel"

Dataset for the paper "Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel".

Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available <a href="https://www.arxiv.org/abs/2402.19037" target="_blank" rel="noopener">online</a>. The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/DL-to-locate-COs-for-SCA">GitHub</a>. The dataset is organized as follows: <ul> <li>\training: contains three subsets, i.e., train, valid, and test.     Each subset consists of two .npy files: <ul> <li>_set: it contains the side-channel traces that are preprocessed accordingly.</li> <li> _labels: itcontains the target labels for training the CNN, labeling each data as cipher start, cipher rest, or noise.</li> </ul> </li> <li>\inference: contains two files as a demo of the inference pipeline.     One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.</li> </ul> Cite: <blockquote> <pre><code>@INPROCEEDINGS{10546758, author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide}, booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, year={2024}, pages={1-6}, doi={10.23919/DATE58400.2024.10546758}}</code></pre> </blockquote> This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license. © 2024 hardware-fab

opencc-by-4.0Nov 2024View details →

Dataset for: Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning

This dataset is part of "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning" [1] available <a href="https://arxiv.org/pdf/2408.06296">online</a>. The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/Hound">GitHub</a>. The dataset is organized as follows: <ul> <li>/training: Contains three subsets: train, valid, and test. Each subset consists of two .npy files: <ul> <li>_set: Contains the preprocessed side-channel traces.</li> <li>_labels: Contains the target labels for training the CNN, labeling each data as `CP start`, `CP spare`, or `noise`.</li> </ul> </li> <li>/inference: Contains files for two demos: consecutive AES executions and AES executions interleaved with noisy applications. Each demo consists of two .npy files: <ul> <li>aes_: Contains the side-channel traces to input into Hound.</li> <li>gt_: Contains the ground truth for checking the correctness of Hound segmentation.</li> </ul> </li> </ul> This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license. © 2024 hardware-fab <blockquote> [1] D. Galli, G. Chiari and D. Zoni, "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces using Deep-Learning," 2024 IEEE 42nd International Conference on Computer Design (ICCD), Milan, Italy, 2024, pp. 114-121, doi: 10.1109/ICCD63220.2024.00027. </blockquote>

opencc-by-4.0Nov 2024View details →

1492 Telegram channels related with Russian - Ukrainian War.

List of 1492 containing media outlets, oficial governmental accounts, influencers and popular channels related with the Russian-Ukranian War. Data include the Channel Names, the Language, Country and Political View of each channel.

opencc-by-4.0Nov 2024View details →

Square Antiprismatic Ion Chelation Is a Key Determinant for Potassium Channel Selectivity

Files presented here are archives K_DB.tar.gz, MEMB_DB.tar.gz and PDB70.tar.gz. Archives  KDB.tar.gz, MEMB_DB.tar.gz and PDB70.tar.gz contain models of indentified sites for potassium channels (dataset #1), other membrane proteins, excluding potassium channels (dataset #2) and   non-membrane proteins form PDB70 (dataset #3). The name of a folder in the dataset corresponds to PDB ID of a protein for which calculation were made. Each folder contain the following files: <ul> <li><PDB_ID>.pdb — the original pdb file.</li> <li><PDB_ID>.ref — file that contains oxygens and nitrogens from original pdb that were used for scanning.</li> <li><PDB_ID>_COMBS.txt — combinations of atoms that were used for calculations.</li> <li><PDB_ID>_alignment_X.pdb — original template that was aligned to the protein atoms. X denotes a number of the alignment.</li> <li><PDB_ID>_site_X.pdb — this pdb file contains eight atoms that form the site for K+ and which were used for the corresponding alignment X.</li> <li><PDB_ID>_RES.txt — the combinations of protein atoms that form the site are written in square brackets. The RMSD value for the alignment to this site is written to the right of them.</li> <li><PDB_ID>_RMSD.log — this file contains RMSD values of the template alignment to the corresponding site.</li> </ul>

opencc-by-4.0Dec 2023View details →

Long-term Continuous Red and Near-infrared Channel Reflectance from MODIS, 2001-2023 (LCREF-MODIS)

Usage Notes: This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark. Key updates in version 3.2 include: <ul> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension: to include observations from the year of 2023.</li> <li>Snow mask: we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. </li> </ul> The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test.  <ul> <li>The LCREF-AVHRR v3.2 (1982-2023) is available at <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> </ul> The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

opencc-by-4.0Dec 2023View details →

Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)

Usage Notes: This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel.  Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test.  <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

opencc-by-4.0Oct 2023View details →

Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada

Dataset representing functional lake-to-channel connectivity in the Mackenzie Delta, NWT, Canada between 1984 and 2022 (final.class_20230324.feather), developed using Landsat 5, 7, and 9 optical imagery.  Data in folders corresponds to data processing steps in scripts: https://doi.org/10.5281/zenodo.14618991 Associated with manuscript: Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada in WRR: Dolan, W., Pavelsky, T. M., & Piliouras, A. (2024). Remote sensing of multitemporal functional lake‐to‐channel connectivity and implications for water movement through the Mackenzie River Delta, Canada. Water Resources Research, 60(4), e2023WR036614. https://doi.org/10.1029/2023WR036614

opencc-by-4.0Jul 2023View details →

Outdoor NB-IoT and 5G coverage and channel information data in urban environments

This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G). Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a>  The dataset includes a metadata file providing the following information for each campaign:  <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization. The dataset contains the following data for NB-IoT: <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city:  data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> The dataset contains the following data for 5G: <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file:  data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches: <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in: L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. DOI: <a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener">10.3390/s23094266</a>. The second interpolation approach is instead introduced and described in: L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation. Positioning using the 5G data was furthermore in investigated in:  K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," IEEE Communications Magazine, Volume 62, Issue 5, pp. 44-49, May 2024. DOI:  <a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener">10.1109/MCOM.011.2200707</a>. G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener">10.1109/OJCOMS.2024.3499370</a>. Please refer to the above publications when using and citing the dataset.