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Data, Volume 6, Issue 6 (June 2021) – 15 articles

Cover Story (view full-size image): For the public sector, Application Programming Interfaces (APIs) could greatly facilitate the exchange of data and digital functionalities in a flexible, controlled and secure way. This first-of-its-kind landscape analysis looks at the main European Commission policy instruments on the adoption of APIs, the available web API standards, government API strategies and cases, and current practices. This research reveals that European governments’ API strategies are rather young, but that policy and associated instruments are emerging. There are well-known API standards and promising practices ready to support the digital transformation of governments through rapid, harmonised and successful adoption of APIs. View this paper
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12 pages, 1563 KiB  
Data Descriptor
Analyses of Li-Rich Minerals Using Handheld LIBS Tool
by Cécile Fabre, Nour Eddine Ourti, Julien Mercadier, Joana Cardoso-Fernandes, Filipa Dias, Mônica Perrotta, Friederike Koerting, Alexandre Lima, Friederike Kaestner, Nicole Koellner, Robert Linnen, David Benn, Tania Martins and Jean Cauzid
Data 2021, 6(6), 68; https://doi.org/10.3390/data6060068 - 21 Jun 2021
Cited by 13 | Viewed by 5096
Abstract
Lithium (Li) is one of the latest metals to be added to the list of critical materials in Europe and, thus, lithium exploration in Europe has become a necessity to guarantee its mid- to long-term stable supply. Laser-induced breakdown spectroscopy (LIBS) is a [...] Read more.
Lithium (Li) is one of the latest metals to be added to the list of critical materials in Europe and, thus, lithium exploration in Europe has become a necessity to guarantee its mid- to long-term stable supply. Laser-induced breakdown spectroscopy (LIBS) is a powerful analysis technique that allows for simultaneous multi-elemental analysis with an excellent coverage of light elements (Z < 13). This data paper provides more than 4000 LIBS spectra obtained using a handheld LIBS tool on approximately 140 Li-content materials (minerals, powder pellets, and rocks) and their Li concentrations. The high resolution of the spectrometers combined with the low detection limits for light elements make the LIBS technique a powerful option to detect Li and trace elements of first interest, such as Be, Cs, F, and Rb. The LIBS spectra dataset combined with the Li content dataset can be used to obtain quantitative estimation of Li in Li-rich matrices. This paper can be utilized as technical and spectroscopic support for Li detection in the field using a portable LIBS instrument. Full article
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<p>(<b>a</b>) Full portable LIBS operating on a pegmatite outcrop; some of the detected elements and the LIBS spectra can be seen on the screen; (<b>b</b>) transfer of the data from the Z300 to the laptop using the Profile Builder interface.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic representation of the analytical protocol for the LIBS analysis on powder pellets, with the five different random zones, nine points for each; (<b>b</b>) images of powder pellets with random areas of analysis; the nine craters can be seen as a square, and the crater size is around 100 µm due to the smoothness of the material.</p> Full article ">Figure 3
<p>LIBS spectra obtained from different Li-rich minerals: amblygonite (4371-ambly-PF-M-0031-A2_2_20191), petalite (4183-peta-LB-R-0013-A_3_2019112), and spodumene (Boa-RS-Spo-M_20190417_043329_PM). (<b>a</b>) Spectra in the entire wavelength range; (<b>b</b>) zoomed image of the Na doublet and Li emission lines; (<b>c</b>) zoomed image of the K and Rb emissions lines from the VNIR. The major element emission lines are shown on the spectra; the LIBS spectra are not normalized.</p> Full article ">
17 pages, 10139 KiB  
Article
Semantic Partitioning and Machine Learning in Sentiment Analysis
by Ebaa Fayyoumi and Sahar Idwan
Data 2021, 6(6), 67; https://doi.org/10.3390/data6060067 - 21 Jun 2021
Cited by 5 | Viewed by 2839
Abstract
This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) [...] Read more.
This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset. Full article
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<p>Traditional Arabic Language (TAL).</p> Full article ">Figure 2
<p>The percentage value of different extracted features used in SA of Jordanian dialect.</p> Full article ">Figure 3
<p>The three domains presented in the collected Jordanian dialect tweets.</p> Full article ">Figure 4
<p>Semantic partitioning Arabic language (SPAL).</p> Full article ">Figure 5
<p>Building the general confusion matrix for the entire classification model of SPAL.</p> Full article ">Figure 6
<p>The number of correctly classified and misclassified tweets for TAL and SPAL models.</p> Full article ">Figure 7
<p>The number of tweet classes (positive and negative) for Jordan based on three regions.</p> Full article ">
25 pages, 3921 KiB  
Data Descriptor
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing
by Ulrike Romatschke, Michael Dixon, Peisang Tsai, Eric Loew, Jothiram Vivekanandan, Jonathan Emmett and Robert Rilling
Data 2021, 6(6), 66; https://doi.org/10.3390/data6060066 - 19 Jun 2021
Cited by 6 | Viewed by 2624
Abstract
The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and [...] Read more.
The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters off Panama and Costa Rica (2019). An extensive set of quality assurance and quality control procedures were developed and applied to all collected data. Engineering measurements yielded calibration characteristics for the antenna, reflector, and radome, which were applied during flight, to produce the radar moments in real-time. Temperature changes in the instrument during flight affect the receiver gains, leading to some bias. Post project, we estimate the temperature-induced gain errors and apply gain corrections to improve the quality of the data. The reflectivity calibration is monitored by comparing sea surface cross-section measurements against theoretically calculated model values. These comparisons indicate that the HCR is calibrated to within 1–2 dB of the theory. A radar echo classification algorithm was developed to identify “cloud echo” and distinguish it from artifacts. Model reanalysis data and digital terrain elevation data were interpolated to the time-range grid of the radar data, to provide an environmental reference. Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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<p>(<b>a</b>) Schematic diagram of the HCR. (<b>b</b>) Image of the HCR pod mounted on the HIAPER aircraft wing. Flight tracks for all research flights in (<b>c</b>) CSET, (<b>d</b>) SOCRATES, and (<b>e</b>) OTREC.</p> Full article ">Figure 2
<p>Typical flight patterns for (<b>a</b>) CSET, (<b>b</b>) SOCRATES, and (<b>c</b>) OTREC.</p> Full article ">Figure 3
<p>FLAG field example. (<b>a</b>) Reflectivity. (<b>b</b>) FLAG. (<b>c</b>) Reflectivity of echo flagged as cloud.</p> Full article ">Figure 4
<p>Example of ERA5 model data interpolated onto the HCR time-range grid. (<b>a</b>) HCR reflectivity and ERA5 0 °C isotherm (light blue line). (<b>b</b>) ERA5 relative humidity.</p> Full article ">Figure 5
<p>Calibration curves for the H channel (red/blue) and V channel (green/magenta). Crosses: measured received power. Lines: measured received power minus estimated noise power. X-axis: input power from signal generator. Y-axis: received power as measured by the digital receiver. Since the receive path is fixed for the HCR, the H and V calibrations apply to both co- and cross-polar measurements.</p> Full article ">Figure 6
<p>Example of a sensitivity histogram for the HCR during OTREC. (<b>a</b>) Reflectivity and (<b>b</b>) SNR for the V channel at a range of 1 km.</p> Full article ">Figure 7
<p>Components of the antenna system, mounted for the calibration test. The scan window shows the extent of the area scanned by the test chamber receiver.</p> Full article ">Figure 8
<p>Example of antenna pattern amplitude for the principal plane in V polarization measured at high (0.5 × wavelength, red) and low (1 × wavelength, blue) grid spacing. The thick red line in the antenna schematic indicates the principal plane.</p> Full article ">Figure 9
<p>Example of an NScal event performed on the ground at the start of SOCRATES Research Flight 01. (<b>a</b>) DBMVC range average (red), raw measured (light blue), and smoothed (dark blue) VLNA temperatures. (<b>b</b>) DBMVC resampled to the temperature resolution (red), DBMVC corrected for VLNA temperature fluctuations (pink), and smoothed VLNA temperature shifted in time (dark blue). (<b>c</b>) Scatter plot of time-shifted VLNA temperatures vs. resampled DBMVC with geometric mean regression line.</p> Full article ">Figure 10
<p>DBMVC minus noise source power vs. pod temperature for all NScal events collected during OTREC. (<b>a</b>) Uncorrected power, (<b>b</b>) after LNA temperature correction with geometric mean regression line, and (<b>c</b>) after LNA and pod temperature corrections. Circles (crosses) denote qualifying (non-qualifying) events.</p> Full article ">Figure 11
<p>Variation of <span class="html-italic">σ</span><sub>0</sub> with (<b>a</b>) surface wind speed and (<b>b</b>) sea surface temperature, calculated with the CM model.</p> Full article ">Figure 12
<p><span class="html-italic">σ</span><sub>0</sub> vs. incidence angle examples of good SScal events. Red (dark blue) lines show observations for the right (left) side of the aircraft, light blue and green lines show model data, and the black line is a fit to the observations. Cases from (<b>a</b>) CSET and (<b>b</b>) OTREC.</p> Full article ">Figure 13
<p><span class="html-italic">σ</span><sub>0</sub> vs. incidence angle examples of SScal events that were removed from the analysis (see text for details). Pink (gray) lines show data collected to the right (left) side of the aircraft, light blue and green lines show model data, and the black line is a fit to the data. Cases from (<b>a</b>–<b>c</b>) OTREC and (<b>d</b>) SOCRATES.</p> Full article ">Figure 14
<p>SScal results for CSET (<b>a</b>,<b>d</b>), SOCRATES (<b>b</b>,<b>e</b>), and OTREC (<b>c</b>,<b>f</b>). Upper panels show the mean measured <span class="html-italic">σ</span><sub>0</sub> (red line) with one standard deviation uncertainty bars. Lower panels show the mean bias and one standard deviation for the FV (light blue), Wu (dark green), and CM models (light green) as well as the means and standard deviations over all incidence angles (see text).</p> Full article ">Figure 15
<p>Example of the radial velocity correction method. (<b>a</b>) Uncorrected radial velocity, (<b>b</b>) radial velocity corrected for aircraft motion and pointing angle deviations, and (<b>c</b>) bias corrected radial velocity in the nadir-pointing data.</p> Full article ">
16 pages, 535 KiB  
Article
Sustainability of Urbanization, Non-Agricultural Output and Air Pollution in the World’s Top 20 Polluting Countries
by Ramesh Chandra Das, Tonmoy Chatterjee and Enrico Ivaldi
Data 2021, 6(6), 65; https://doi.org/10.3390/data6060065 - 17 Jun 2021
Cited by 5 | Viewed by 2790
Abstract
Rapid urbanization is being increasingly recognized as a significant factor of environmental pollution across the world. However, the significance of sustainable urbanization in controlling both pollution and population remains either limited in scope, in the case of developed countries, or less researched, in [...] Read more.
Rapid urbanization is being increasingly recognized as a significant factor of environmental pollution across the world. However, the significance of sustainable urbanization in controlling both pollution and population remains either limited in scope, in the case of developed countries, or less researched, in the case of developing nations. To fill this gap, the present study employed both theoretical and empirical tools to investigate the significant link between sustainable urbanization, pollution and non-agricultural output. In order to empirically examine the supposed link among the key variables mentioned above, the present study considered a panel of the world’s top 20 polluting countries for the 1991–2018 period, which significantly includes both developed and developing nations. Panel vector error correction model and panel co-integration techniques were employed to derive the possible correlation between the variables through sustainable urbanization. Empirical findings show an absence of equilibrium relations among the three variables in the panel of developed countries. However, the study clearly finds that all the three indicators maintain long-run associations for the panel of developing countries. Furthermore, in the short run, the results determine unambiguously that there are significant causal interplays between any two sets of variables and the remaining one variable for both the panel data of developed and developing countries. On the other hand, short-run interplays among the variables we considered exist for both developed and developing economies. From the perspective of policy formulation, the present study shows that policy makers from both the developed and developing nations should be cautious before encouraging urbanization, at least in the short term. However, the combined effects in the short and long term suggest policy makers should be more careful before encouraging urbanization in developing economies. Full article
(This article belongs to the Special Issue Sustainability and Innovation in Entrepreneurial Ecosystems: Data-Driven Approaches)
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<p>Map showing geographical locations of the countries. Notes: The panel of developed economies (in blue color) included 11 countries: U.S., Canada, Germany, U.K., France, Italy, Japan, South Korea, Saudi Arabia, Poland and Australia. The panel of developing economies (in red color) included 9 countries: China, Russia, India, Brazil, Mexico, South Africa, Indonesia, Turkey and Iran.</p> Full article ">
9 pages, 1049 KiB  
Data Descriptor
A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period
by Sara Melotte and Mayank Kejriwal
Data 2021, 6(6), 64; https://doi.org/10.3390/data6060064 - 16 Jun 2021
Cited by 9 | Viewed by 6370
Abstract
One of the unfortunate findings from the ongoing COVID-19 crisis is the disproportionate impact the crisis has had on people and communities who were already socioeconomically disadvantaged. It has, however, been difficult to study this issue at scale and in greater detail using [...] Read more.
One of the unfortunate findings from the ongoing COVID-19 crisis is the disproportionate impact the crisis has had on people and communities who were already socioeconomically disadvantaged. It has, however, been difficult to study this issue at scale and in greater detail using social media platforms like Twitter. Several COVID-19 Twitter datasets have been released, but they have very broad scope, both topically and geographically. In this paper, we present a more controlled and compact dataset that can be used to answer a range of potential research questions (especially pertaining to computational social science) without requiring extensive preprocessing or tweet-hydration from the earlier datasets. The proposed dataset comprises tens of thousands of geotagged (and in many cases, reverse-geocoded) tweets originally collected over a 255-day period in 2020 over 10 metropolitan areas in North America. Since there are socioeconomic disparities within these cities (sometimes to an extreme extent, as witnessed in ‘inner city neighborhoods’ in some of these cities), the dataset can be used to assess such socioeconomic disparities from a social media lens, in addition to comparing and contrasting behavior across cities. Full article
(This article belongs to the Section Information Systems and Data Management)
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<p>An example JSON dictionary fragment representing a tweet (originating in Los Angeles) in our dataset, with the metadata.</p> Full article ">Figure 2
<p>A workflow illustrating the methodology behind data processing and collection as applied to the underlying GeoCOV19Tweets dataset to obtain the proposed dataset.</p> Full article ">Figure 3
<p>Bounding rectangles for New York, Los Angeles, Toronto, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, and Dallas.</p> Full article ">Figure 4
<p>Sentiment scores versus month over all the tweets in our dataset. We also illustrate (for each month) the metropolitan areas with the lowest and highest average sentiment scores.</p> Full article ">Figure 5
<p>The 10 most prevalent hashtags (determined over all the tweets in our dataset). We also illustrate, for each hashtag, the metropolitan area with the lowest and highest prevalence in the corresponding metropolitan area.</p> Full article ">
15 pages, 4043 KiB  
Data Descriptor
A Disease Control-Oriented Land Cover Land Use Map for Myanmar
by Dong Chen, Varada Shevade, Allison Baer, Jiaying He, Amanda Hoffman-Hall, Qing Ying, Yao Li and Tatiana V. Loboda
Data 2021, 6(6), 63; https://doi.org/10.3390/data6060063 - 13 Jun 2021
Cited by 6 | Viewed by 4448
Abstract
Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar [...] Read more.
Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries. Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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<p>Overview of the presented LCLU map for Myanmar. Insets 1-4 shows zoomed-in views of 4 locations, respectively.</p> Full article ">Figure 2
<p>Distribution in terms of total area of the ten classes contained in the presented LCLU map.</p> Full article ">Figure 3
<p>A comparison between the current product and three existing land cover products in terms of the representation of human presence (SERVIR Mekong [<a href="#B19-data-06-00063" class="html-bibr">19</a>]: urban and built-up; HBASE [<a href="#B21-data-06-00063" class="html-bibr">21</a>]: built-up and settlement; GHSL [<a href="#B20-data-06-00063" class="html-bibr">20</a>]: built-up; current product: impervious surface and villages). All other classes irrelevant to human presence in the compared products are masked out (indicated in black). The SERVIR Mekong land cover map that was compared and displayed was produced for 2016 (the same year as our product).</p> Full article ">Figure 4
<p>Workflow for the development of the presented dataset. The purple boxes with dashed line boundaries indicate the three major technical steps (corresponding to the subsections of Methods). The blue and green boxes indicate the major data inputs and intermediate outputs, respectively. The orange box represents the final output, which is the produced LCLU map. Abbreviations: a Shuttle Radar Topography Mission (SRTM), a digital elevation model (DEM), a Moderate Resolution Imaging Spectroradiometer (MODIS), a Visible Infrared Imaging Radiometer Suite (VIIRS), the Landsat Vegetation Continuous Fields (Landsat VCF) project [<a href="#B25-data-06-00063" class="html-bibr">25</a>], Very High Resolution (VHR), the global surface water dynamics (GSWD) product, the Global Man-made Impervious Surface (GMIS) product [<a href="#B26-data-06-00063" class="html-bibr">26</a>], the Global Food Security Analysis-Support (GFSAD) product [<a href="#B27-data-06-00063" class="html-bibr">27</a>], the Global Forest Change (GFC) product [<a href="#B25-data-06-00063" class="html-bibr">25</a>], the normalized difference vegetation index (NDVI) [<a href="#B28-data-06-00063" class="html-bibr">28</a>], the global bare ground gain (GBG) product [<a href="#B29-data-06-00063" class="html-bibr">29</a>].</p> Full article ">Figure 5
<p>The digitized road network (in red lines) in comparison to the OpenStreetMap road network [<a href="#B30-data-06-00063" class="html-bibr">30</a>] (in blue lines) of Myanmar, with edits to the dataset made as necessary. Panels one through four show comparisons of the digitized road network and the OSM road network of (1) Bhamo Township in the Kachin State, (2) Demoso Township in the Kayah State, (3) Hakha Township in the Chin State, and (4) Kyunhla Township in the Sagaing Region. These four regions were selected because they were among the areas that have the highest number of roads (in distance) added and represent different parts of the country. Administrative boundaries are from MIMU.</p> Full article ">
20 pages, 3914 KiB  
Data Descriptor
Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor
by Pavel Masek, Martin Stusek, Ekaterina Svertoka, Jan Pospisil, Radim Burget, Elena Simona Lohan, Ion Marghescu, Jiri Hosek and Aleksandr Ometov
Data 2021, 6(6), 62; https://doi.org/10.3390/data6060062 - 10 Jun 2021
Cited by 13 | Viewed by 4659
Abstract
This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It [...] Read more.
This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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<p>The network architecture of the LoRaWAN network.</p> Full article ">Figure 2
<p>Summary diagram of ED’s behavior for LoRaWAN.</p> Full article ">Figure 3
<p>Evaluation board constructed for testing LPWA technologies.</p> Full article ">Figure 4
<p>Durability measurements of devices in different climatic conditions in the temperature chamber Vötsch VC3 7018.</p> Full article ">Figure 5
<p>Current consumption during 50 B message transmission.</p> Full article ">Figure 6
<p>Unconfirmed transmission power consumption.</p> Full article ">Figure 7
<p>Confirmed transmission power consumption.</p> Full article ">Figure 8
<p>Measurement locations in Brno, Czech Republic.</p> Full article ">Figure 9
<p>Effect of the distance to the nearest GW on the outage probability.</p> Full article ">Figure 10
<p>Observed relation of RSSI and distance per successfully delivered packet.</p> Full article ">Figure 11
<p>Observed relation of SNR and distance per successfully delivered packet.</p> Full article ">Figure 12
<p>Observed relation of SNR and RSSI per successfully delivered packet.</p> Full article ">Figure 13
<p>BUT sensor RSSI samples relative frequency histogram.</p> Full article ">Figure 14
<p>City center sensor RSSI samples relative frequency histogram.</p> Full article ">Figure 15
<p>BUT sensor SNR samples relative frequency histogram.</p> Full article ">Figure 16
<p>City center sensor SNR samples relative frequency histogram.</p> Full article ">
26 pages, 809 KiB  
Article
A Framework Using Contrastive Learning for Classification with Noisy Labels
by Madalina Ciortan, Romain Dupuis and Thomas Peel
Data 2021, 6(6), 61; https://doi.org/10.3390/data6060061 - 9 Jun 2021
Cited by 7 | Viewed by 3594
Abstract
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have, been combined into a fine-tuning [...] Read more.
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have, been combined into a fine-tuning phase following the pre-training. In this paper, we provide an extensive empirical study showing that a preliminary contrastive learning step brings a significant gain in performance when using different loss functions: non robust, robust, and early-learning regularized. Our experiments performed on standard benchmarks and real-world datasets demonstrate that: (i) the contrastive pre-training increases the robustness of any loss function to noisy labels and (ii) the additional fine-tuning phase can further improve accuracy, but at the cost of additional complexity. Full article
(This article belongs to the Special Issue Machine Learning with Label Noise)
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<p>Top-1 test accuracy for a ResNet18 trained on the CIFAR-100 dataset with a symmetric noise of 80% for three losses: Cross Entropy (CE), Normalized Focal Loss + Reverse Cross Entropy (NFL+RCE), and Early Learning Regularization (ELR).</p> Full article ">Figure 2
<p>Overview of the framework consisting of two phases: pre-training (panel <b>a</b>) and fine-tuning (panel <b>b</b>). After a contrastive learning phase (<b>a1</b>), a classifier (<b>a2</b>) is trained to predict train-set pseudo-labels <math display="inline"><semantics> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> </semantics></math>. The fine-tuning phase uses <math display="inline"><semantics> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> </semantics></math> as a new ground truth. First, a GMM model (<b>b1</b>) predicts the probability of correctness for each sample, used as a corrective weight factor in a supervised contrastive training (panel <b>b2</b>). The final predictions <math display="inline"><semantics> <msub> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mrow> <mi>f</mi> <mi>i</mi> <mi>n</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> are produced by the (<b>b3</b>) classifier.</p> Full article ">Figure 3
<p>Accuracy of pseudo labels on all simulated settings with asymmetric (<b>a</b>) and symmetric (<b>b</b>) noise, evaluated on CIFAR100. The correctness of the ground truth is represented on the x-axis, while the accuracy of predicted pseudo labels on the y-axis. In all experiments, the pseudo labels have a higher accuracy than the corrupted ground truth and this gain increases with the noise ratio.</p> Full article ">Figure 4
<p>Accuracy of the entire training set (in blue) compared to the clean train subset (in red); the clean subset’s percentual size is depicted in green. The example is performed on CIFAR100, with 40% symmetric noise.</p> Full article ">Figure 5
<p>Learning rate sensitivity for CIFAR100 with 80% noise. The explored learning rate values are <math display="inline"><semantics> <mrow> <mo>{</mo> <mn>0.001</mn> <mo>,</mo> <mn>0.01</mn> <mo>,</mo> <mn>0.1</mn> <mo>,</mo> <mn>1.0</mn> <mo>}</mo> </mrow> </semantics></math>. The baseline (dashed line) is compared with our framework (solid line).</p> Full article ">Figure 6
<p>Accuracy gain when performing the fine-tuning phase after the pre-training block (computed as the difference between fine-tuning accuracy and pre-training accuracy). The plot gathers the results for all noise ratios on CIFAR10 (panels <b>a</b>,<b>b</b>) and CIFAR100 (<b>c</b>,<b>d</b>) with symmetric (first column) and asymmetric (second column) noise.</p> Full article ">Figure 7
<p>Top-1 accuracy gain for the dynamic bootstrapping on CIFAR100 with asymmetric (<b>a</b>) and symmetric noise (<b>b</b>). Dynamic bootstrapping is an alternative to the proposed fine-tuning phase. Each color is associated to a noise ratio.</p> Full article ">Figure A1
<p>Hyperparameter sensitivity for CIFAR100.</p> Full article ">Figure A1 Cont.
<p>Hyperparameter sensitivity for CIFAR100.</p> Full article ">Figure A2
<p>Gain in performance when using a supplementary classifier warm-up phase before training the entire model on CIFAR 100 with symmetric (panel <b>a</b>) and asymmetric noise (panel <b>b</b>).</p> Full article ">Figure A3
<p>Evolution of accuracy across train/validation/test sets. (<b>a</b>) Prediction stability on the validation set computed as the number of samples changing class across consecutive epochs. We compared the stability of predictions (in red) with the accuracy of the clean test set (in blue). (<b>b</b>) The rolling mean average of the number of predictions has been depicted in black. The experiments have been performed on CIFAR 100, with 80% symmetric noise during the first classification phase and used NFL+RCE loss. In this plot, only the test set has correct labels. The accuracy on the corrupted validation set reflects the noise level while on the corrupted train set the increase in accuracy corresponds to overfitting (memorization of incorrect labels).</p> Full article ">Figure A4
<p>Evolution of accuracy across train/validation/test sets (<b>a</b>) Prediction stability on the validation set computed as the number of samples changing class across consecutive epochs. We compared the stability of predictions (in red) with the accuracy of the clean test set (in blue). (<b>b</b>) The rolling mean average of the number of predictions has been depicted in black. The experiments have been performed on CIFAR 100, with 40% asymmetric noise during the first classification phase and used NFL+RCE loss. In this plot, only the test set uses correct labels. The accuracy on the corrupted validation set reflects the level of label noise in the data, while on the corrupted train set the increase in accuracy corresponds to overfitting (memorization of incorrect labels).</p> Full article ">Figure A5
<p>Evolution of train loss and test accuracy on CIFAR, 60% symmetric noise. The theoretical conditions of higher variance on the train loss, associated with the start of the memorization phase, as suggested by TSP, are not fulfilled.</p> Full article ">Figure A6
<p>CKA similarity for a model trained with the NFL+RCE loss function on CIFAR100 with <math display="inline"><semantics> <mrow> <mn>80</mn> <mo>%</mo> </mrow> </semantics></math> noise.</p> Full article ">
30 pages, 4131 KiB  
Article
Information Quality Assessment for Data Fusion Systems
by Miguel A. Becerra, Catalina Tobón, Andrés Eduardo Castro-Ospina and Diego H. Peluffo-Ordóñez
Data 2021, 6(6), 60; https://doi.org/10.3390/data6060060 - 8 Jun 2021
Cited by 18 | Viewed by 5327
Abstract
This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and [...] Read more.
This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered. Full article
(This article belongs to the Section Information Systems and Data Management)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Taxonomy of data fusion models clustered into three categories: data, activity, and role. The situation awareness model encompasses various models, and even though it was included in the data cluster, it can belong to a specific group depending on the model.</p> Full article ">Figure 2
<p>Number of Scopus publications for each data fusion model per year from 1975 to 2021. The x-axis corresponds to the time window from the appearance of the first data fusion model according to the here-made up-to-date search in Scopus. For each year on the x-axis, the number of citations for each model is shown on the y-axis making distinction with different colors. The first appearance of each model in this graphic is limited to the search in Scopus and does not correspond to the year of publication of the data fusion model. Some models were published earlier than their appearance in Scopus and may also be available in other databases.</p> Full article ">Figure 3
<p>Joint Directors of Laboratories (JDL) model.</p> Full article ">Figure 4
<p>Taxonomy of the methodologies of information quality-based data fusion techniques.</p> Full article ">Figure 5
<p>Information quality criteria categories proposed by different authors from 1996 to 2016 [<a href="#B45-data-06-00060" class="html-bibr">45</a>,<a href="#B86-data-06-00060" class="html-bibr">86</a>,<a href="#B91-data-06-00060" class="html-bibr">91</a>,<a href="#B95-data-06-00060" class="html-bibr">95</a>,<a href="#B96-data-06-00060" class="html-bibr">96</a>,<a href="#B97-data-06-00060" class="html-bibr">97</a>,<a href="#B98-data-06-00060" class="html-bibr">98</a>]. Each category includes various criteria.</p> Full article ">Figure 6
<p>Correlation between information quality criteria.</p> Full article ">Figure 7
<p>Methodology introduced/proposed by Todoran et al.</p> Full article ">Figure 8
<p>Block diagram to describe the decomposition of the IFS into elementary modules and the generation of IQ transfer functions for each module. This figure illustrates the decomposition (serial, parallel, or both) of a data fusion system into its elementary modules. In addition, a quality transfer function is obtained for each module by applying analytical or nonanalytical functions using information quality measures and input and output data of each module.</p> Full article ">Figure 9
<p>Components of information quality in the Joint Directors of Laboratories (JDL) model.</p> Full article ">Figure 10
<p>Summarized Information Quality (IQ) assessment of the methodology proposed in [<a href="#B45-data-06-00060" class="html-bibr">45</a>]. This assessment includes the relationships between three criteria groups and considers the effects and evaluation of context. In addition, the results of the IQ assessment can be evaluated by IQ criteria that can be taken from the same three groups.</p> Full article ">
20 pages, 946 KiB  
Article
APIs for EU Governments: A Landscape Analysis on Policy Instruments, Standards, Strategies and Best Practices
by Lorenzino Vaccari, Monica Posada, Mark Boyd and Mattia Santoro
Data 2021, 6(6), 59; https://doi.org/10.3390/data6060059 - 8 Jun 2021
Cited by 3 | Viewed by 5857
Abstract
Application Programming Interfaces (APIs) could greatly facilitate the exchange of data and functionalities between software applications in a flexible, controlled and secure way, especially on the web. Private companies, from startups to enterprises, have been using APIs for several years now, but it [...] Read more.
Application Programming Interfaces (APIs) could greatly facilitate the exchange of data and functionalities between software applications in a flexible, controlled and secure way, especially on the web. Private companies, from startups to enterprises, have been using APIs for several years now, but it is only recently that APIs have seen increased interest in the public sector. API adoption in the public sector faces organisational, technical, legal and economic obstacles, and to overcome these barriers, proposed methods from the private sector and early adopters in the public sector provide a way forward. The available documentation is often sparse, difficult to find and to reuse for new contexts. No past efforts to collect and analyse these resources have been made. To address this shortcoming, this paper describes a landscape analysis in four areas: the main European Commission policy instruments on the adoption of APIs, the available web API standards, a set of European government API strategies and cases, and a list of government proposed methods distilled from more than 3900 documents. Our results reveal that European policy legislation and associated instruments promote, and in some cases mandate, the use of APIs, and that governments’ API strategies in the European Union are rather young but also that there are well known web APIs standards and proposed methods ready to support the digital transformation of governments through rapid, harmonised and successful adoption of APIs. Full article
(This article belongs to the Special Issue A European Approach to the Establishment of Data Spaces)
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<p>Number of technical specifications and standards per category (source: authors’ elaboration based on [<a href="#B4-data-06-00059" class="html-bibr">4</a>]).</p> Full article ">Figure 2
<p>API strategies maturity level (source: authors’ elaboration).</p> Full article ">Figure 3
<p>Adoption of Web APIs. <b>Left</b> panel: cumulative count of the number of web API. <b>Right</b> Panel: cumulative count of the number of the most popular APIs by category (source: authors’ elaboration based on ProgrammableWeb.com data of June 2019 [<a href="#B4-data-06-00059" class="html-bibr">4</a>]).</p> Full article ">Figure 4
<p>Types of APIs in analysed API cases (N = 219) (source: authors’ elaboration based on [<a href="#B79-data-06-00059" class="html-bibr">79</a>]).</p> Full article ">Figure 5
<p>APIs classified by theme (N = 219) (source: authors’ elaboration based on [<a href="#B79-data-06-00059" class="html-bibr">79</a>]).</p> Full article ">Figure 6
<p>APIs literature by topic and target level (N = 343) (Source: Authors’ elaboration, based on [<a href="#B79-data-06-00059" class="html-bibr">79</a>]).</p> Full article ">
7 pages, 1117 KiB  
Data Descriptor
A Large-Scale Dataset of Barley, Maize and Sorghum Variety Identification Using DNA Fingerprinting in Ethiopia
by Frederic Kosmowski, Alemayehu Ambel, Asmelash H. Tsegay, Alemayehu Teressa Negawo, Jason Carling, Andrzej Kilian and The Central Statistics Agency
Data 2021, 6(6), 58; https://doi.org/10.3390/data6060058 - 3 Jun 2021
Cited by 2 | Viewed by 3105
Abstract
The data described in this paper were part of a large-scale nationally representative household survey, the Ethiopian Socioeconomic Survey (ESS 2018/19). Grain samples of barley, maize and sorghum were collected in six regions in Ethiopia. Variety identification was assessed by matching samples to [...] Read more.
The data described in this paper were part of a large-scale nationally representative household survey, the Ethiopian Socioeconomic Survey (ESS 2018/19). Grain samples of barley, maize and sorghum were collected in six regions in Ethiopia. Variety identification was assessed by matching samples to a reference library composed of released improved materials, using approximately 50,000 markers from DArTseq platforms. This data were part of a study documenting the reach of CGIAR-related germplasms in Ethiopia. These objective measures of crop varietal adoption, unique in the public domain, can be analyzed along with a large set of variables related to agro-ecologies, household characteristics and plot management practices, available in the Ethiopian Socioeconomic Survey 2018/19. Full article
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<p>Density plot of grain purity per crop.</p> Full article ">Figure 2
<p>Distribution of varieties identified through DNA fingerprinting: (<b>a</b>) barley; (<b>b</b>) maize; (<b>c</b>) sorghum. * Varieties identified on five samples or less are gathered. These are available in the dataset.</p> Full article ">
15 pages, 8308 KiB  
Data Descriptor
Dataset for the Solar Incident Radiation and Electricity Production BIPV/BAPV System on the Northern/Southern Façade in Dense Urban Areas
by Hassan Gholami and Harald Nils Røstvik
Data 2021, 6(6), 57; https://doi.org/10.3390/data6060057 - 26 May 2021
Cited by 5 | Viewed by 3328
Abstract
The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the [...] Read more.
The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations. Full article
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Graphical abstract
Full article ">Figure 1
<p>A picture of the site with components.</p> Full article ">Figure 1 Cont.
<p>A picture of the site with components.</p> Full article ">Figure 2
<p>The implementation phase of PV panels in front of glass cladding.</p> Full article ">Figure 3
<p>The panel cladding installation phase.</p> Full article ">Figure 4
<p>Implementation of irradiation measuring equipment.</p> Full article ">Figure 5
<p>Electricity production of each PV panel on a sunny day of each month (February–November).</p> Full article ">Figure 5 Cont.
<p>Electricity production of each PV panel on a sunny day of each month (February–November).</p> Full article ">Figure 5 Cont.
<p>Electricity production of each PV panel on a sunny day of each month (February–November).</p> Full article ">Figure 5 Cont.
<p>Electricity production of each PV panel on a sunny day of each month (February–November).</p> Full article ">Figure 6
<p>Solar incident radiation on each PV panel on a sunny day of each month (June–November).</p> Full article ">Figure 6 Cont.
<p>Solar incident radiation on each PV panel on a sunny day of each month (June–November).</p> Full article ">Figure 6 Cont.
<p>Solar incident radiation on each PV panel on a sunny day of each month (June–November).</p> Full article ">Figure 7
<p>The average efficiency of the PV panels in a clear sky condition.</p> Full article ">Figure 8
<p>Recorded peak power production of each panel during the monitoring time.</p> Full article ">
13 pages, 3635 KiB  
Article
Automation of Work Processes and Night Work
by Urška Kosem, Mirko Markič and Annmarie Gorenc Zoran
Data 2021, 6(6), 56; https://doi.org/10.3390/data6060056 - 26 May 2021
Cited by 2 | Viewed by 3416
Abstract
Background: Automation of production processes is not just a simple replacement of a person in production, but it should lead to the success of an organization and contribute to the sustainable development of society and the natural environment. The aim of our study [...] Read more.
Background: Automation of production processes is not just a simple replacement of a person in production, but it should lead to the success of an organization and contribute to the sustainable development of society and the natural environment. The aim of our study was to find out whether the level of automation of production processes affects the proportion of night work hours of production workers and whether employers are willing to automate production processes to achieve a lower number of night work hours. Methods: We used a quantitative approach to collect primary data through the survey method. The questionnaire was completed by 502 large and medium-sized manufacturing companies in Slovenia. Results: We found no statistically significant correlation between the level of automation of production processes and the percentage of night work hours of production workers. We also found that the reduction of the proportion of night work does not appear to be the main motivator for the introduction of automation of production processes. Conclusions: Based on the results, we rejected the assumption that automation of production processes has a direct impact on the proportion of night work. Moreover, our study will benefit all those who are concerned with the automation of production processes and night work. Full article
(This article belongs to the Special Issue Development of a Smart Future under Society 5.0)
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<p>Level of Automation Formula.</p> Full article ">Figure 2
<p>Level of Automation and Proportion of Night Work.</p> Full article ">Figure 3
<p>Motivators for Automation in Companies that Run the Night Shift.</p> Full article ">
30 pages, 4019 KiB  
Review
Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review
by Giuseppe Ciaburro and Gino Iannace
Data 2021, 6(6), 55; https://doi.org/10.3390/data6060055 - 25 May 2021
Cited by 23 | Viewed by 10567
Abstract
To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time [...] Read more.
To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data. Full article
(This article belongs to the Special Issue Knowledge Extraction from Data Using Machine Learning)
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<p>Trend of milk production monitored from January 1962 to December 1975.</p> Full article ">Figure 2
<p>Times series components: (<b>a</b>) trend; (<b>b</b>) seasonality; (<b>c</b>) cycle; (<b>d</b>) disturbance.</p> Full article ">Figure 3
<p>Block diagram of the machine-learning-based knowledge extraction for time series forecasting.</p> Full article ">Figure 4
<p>Artificial neural network architecture for time series data. The input dataset is divided into sequential groups with a fixed number of components. Each of these vectors constitutes a network input, to which the correct output is matched. Therefore, neuron 1 will be sent the vector that contains the first k data (from <span class="html-italic">x</span><sub>1</sub> to <span class="html-italic">x<sub>k</sub></span>), neuron 2 instead will be sent the vector that contains data from <span class="html-italic">x<sub>2</sub></span> to <span class="html-italic">x<sub>k</sub></span> <sub>+ 1</sub>, and so on (Equation (5)).</p> Full article ">Figure 5
<p>Artificial neural network training procedure.</p> Full article ">Figure 6
<p>Clustering grouping methodology example in a new plane defined by two new dimensions identified by the algorithm.</p> Full article ">Figure 7
<p>Dendrogram representation with nodes, claves, and leaves.</p> Full article ">Figure 8
<p>An example of clustering of a dataset using two classes in a new bidimensional plane.</p> Full article ">Figure 9
<p>Convolutional neural network architecture.</p> Full article ">Figure 10
<p>Recurrent neural network architecture.</p> Full article ">Figure 11
<p>Recurrent neural network unfolded scheme.</p> Full article ">
5 pages, 567 KiB  
Data Descriptor
Data on the Quantification of Aspartate, GABA and Glutamine Levels in the Spinal Cord of Larval Sea Lampreys after a Complete Spinal Cord Injury
by Blanca Fernández-López, Natividad Pereiro, Anunciación Lafuente, María Celina Rodicio and Antón Barreiro-Iglesias
Data 2021, 6(6), 54; https://doi.org/10.3390/data6060054 - 24 May 2021
Cited by 1 | Viewed by 2111
Abstract
We used high-performance liquid chromatography (HPLC) methods to quantify aspartate, GABA, and glutamine levels in the spinal cord of larval sea lampreys following a complete spinal cord injury. Mature larval sea lampreys recover spontaneously from a complete spinal cord transection and the changes [...] Read more.
We used high-performance liquid chromatography (HPLC) methods to quantify aspartate, GABA, and glutamine levels in the spinal cord of larval sea lampreys following a complete spinal cord injury. Mature larval sea lampreys recover spontaneously from a complete spinal cord transection and the changes in neurotransmitter systems after spinal cord injury might be related to their amazing regenerative capabilities. The data presented here show the concentration of the aminoacidergic neurotransmitters GABA (and its precursor glutamine) and aspartate in the spinal cord of control (non-injured) and 2-, 4-, and 10-week post-lesion animals. Statistical analyses showed that GABA and aspartate levels significantly increase in the spinal cord four weeks after a complete spinal cord injury and that glutamine levels decrease 10 weeks after injury as compared to controls. These data might be of interest to those studying the role of neurotransmitters and neuromodulators in recovery from spinal cord injury in vertebrates. Full article
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<p>(<b>A</b>) Graph showing aspartate concentration in the spinal cord of control and injured animals (control: 0.001288 ± 0.0002611 ng aspartate/mg protein; 2 wpl: 0.002647 ± 0.0005217 ng aspartate/mg protein; 4 wpl: 0.00422 ± 0.00079 ng aspartate/mg protein; 10 wpl: 0.0008227 ± 6.944 × 10<sup>−5</sup> ng aspartate/mg protein). Note the significant increase (asterisks) in aspartate concentration at 4 wpl (Dunnett’s multiple comparisons test; <span class="html-italic">p</span> = 0.0018). (<b>B</b>) Graph showing GABA concentration in the spinal cord of control and injured animals (control: 0.002721 ± 0.0004307 ng GABA/mg protein; 2 wpl: 0.002958 ± 0.0006070 ng GABA/mg protein; 4 wpl: 0.006030 ± 0.0009828 ng GABA/mg protein; 10 wpl: 0.001109 ± 7.763 × 10<sup>−5</sup> ng GABA/mg protein). Note the significant increase (asterisks) in GABA concentration at 4 wpl (Dunnett’s multiple comparisons test; <span class="html-italic">p</span> = 0.0044). (<b>C</b>) Graph showing glutamine concentration in the spinal cord of control and injured animals (control: 0.003933 ± 0.0008003 ng glutamine/mg protein; 2 wpl: 0.002228 ± 0.0004552 ng glutamine/mg protein; 4 wpl: 0.003610 ± 0.0006883 ng glutamine/mg protein; 10 wpl: 0.0007130 ± 7.339 × 10<sup>−5</sup> ng glutamine/mg protein). Note the significant decrease (asterisks) in glutamine concentration at 10 wpl (Dunnett’s multiple comparisons test; <span class="html-italic">p</span> = 0.0031).</p> Full article ">
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