Team Algorithm Research Articles

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Comparative Analysis of Sea Ice Between the Antarctic and the Arctic

Polar regions have a profound impact on global climate change, because polar regions are one of the world’s largest sources of cold. Sea ice is the main influencing factor in the polar regions, which has a great influence on the heat transfer, which in turn affects the global climate system. In the past two decades, Arctic sea ice has decreased year by year, while Antarctic sea ice has increased. The use of passive satellite remote sensing data for long-term observation and analysis of polar sea ice will provide an important theoretical basis for studying global climate change and an important data source for polar sea ice research. This study uses the data of the Antarctic and Arctic sea ice density data from 1998 to 2018 to compare and analyze the changes in time and space to find out the changes of the sea ice in the south and Arctic. Using the SSM / I data of the US National Defense Meteorological Satellite and the NASA TEAM algorithm, the sea ice data from 1998 to 2018 was compared and analyzed. The specific process is to use the NASA TEAM algorithm to invert the Arctic and Arctic sea ice, obtain multi-year ice, one-year ice, and overall sea ice density data, and then determine the sea ice area. Through comparative analysis, it is found that the area of Antarctic sea ice is significantly larger than that of the North Pole, and the change trend of the two is similar. The downward trend is greater than that of the South Pole; the total annual ice volume of the South Pole is greater than that of the North Pole, both of which have an upward trend, and the Arctic has a greater increase; the overall sea ice has fallen, and the multi-year ice has decreased while the annual ice has increased. Sea ice has a tendency to transform from multi-year ice to one-year ice; Antarctic multi-year ice is mainly distributed in the Weddell Sea, with stable areas accounting for 60% of the multi-year ice area; Antarctica is dominated by one-year ice, and one-year ice stable area accounts for one 80.1% of the total annual ice area; Arctic multi-year ice mainly exists in the central area of the Arctic Ocean. The Arctic multi-year ice stable area only accounts for 35.06%; the Arctic one-year ice is mainly distributed around the multi-year ice periphery, and the stable existence area accounts for the total annual ice area 48.5%.

Integrating NASA Aqua AIRS in a Real‐Time NUCAPS Science‐To‐Applications System to Support Severe Weather Forecasting

AbstractIn recent years, National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral infrared satellite sounding retrievals derived from Joint Polar Satellite System (JPSS) polar‐orbiting satellites have been documented as observations that add value to weather forecasting applications. NUCAPS is currently the operational algorithm delivering JPSS satellite sounding retrievals to the NOAA National Weather Service (NWS) and is based on the heritage Atmospheric Infrared Sounder (AIRS) Science Team algorithm for processing vertical temperature, moisture, and trace gas retrievals. For the Special Collection on “Twenty Years of Observations from AIRS,” we highlight the methodology we implemented to develop a prototype science‐to‐applications system to enable real‐time processing of AIRS satellite sounding retrievals through the NUCAPS algorithm (i.e., NUCAPS‐Aqua) to support weather forecasting applications. The addition of NUCAPS‐Aqua to experimental real‐time pathways alongside operational JPSS NUCAPS soundings, facilitated assessment of NUCAPS‐Aqua at the 2022 Hazardous Weather Testbed (HWT) Spring Experiment. Development of NUCAPS‐Aqua described in this technical report includes preservation of microwave observations and calculation of a‐priori regression coefficients. Additionally, the real‐time processing and challenges with implementing a science‐to‐applications system are discussed. Two illustrative pre‐convective forecasting examples analyzed by NWS forecasters during the 2022 HWT Spring Experiment are highlighted to demonstrate the benefit of NUCAPS‐Aqua as (a) special afternoon soundings, (b) an additional observation to assess temporal trends using multiple satellites, and (c) a complement to observational and model analysis.

Evaluation of Sea Ice Concentration Data Using Dual-Polarized Ratio Algorithm in Comparison With Other Satellite Passive Microwave Sea Ice Concentration Data Sets and Ship-Based Visual Observations

The dual-polarized ratio (DPR) algorithm is a new algorithm that enable calculation of Arctic sea ice concentration from the 36.5-GHz channel of the sensor Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2). In this paper, we demonstrate results that the sea ice concentration data using DPR algorithm (DPR-AMSR) are evaluated and compared with other eight Arctic sea ice concentration data products with respect to differences in sea ice concentration, sea ice area, and sea ice extent. On a pan-Arctic scale, the evaluation results are mostly very similar between DPR-AMSR and the bootstrap algorithm from AMSR-E/AMSR2 (BT-AMSR), the bootstrap algorithm from SSM/I or SSMIS (BT-SSMI), the ARTIST Sea Ice algorithm from AMSR-E/AMSR2 (ASI-AMSR), and the enhanced NASA Team algorithm from AMSR-E/AMSR2 (NT2-AMSR). Among of these products, the differences in sea ice concentration agree within ±5%. However, European Space Agency Climate Change Initiative algorithm from AMSR-E/AMSR2 (SICCI-AMSR), the European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility from SSM/I or SSMIS (OSI-SSMI), the ARTIST Sea Ice algorithm from SSM/I or SSMIS (ASI-SSMI), and the NASA Team algorithm from SSM/I or SSMIS (NT1-SSMI) are all lower than DPR-AMSR at sea ice edge. And NT1-SSMI had the largest negative difference, which was lower than -15% or even 20%.The difference of sea ice area was consistently within ±0.5 million km2 between DPR-AMSR and BT-AMSR, BT-SSMI, ASI-AMSR, and NT2-AMSR in all years. The smallest difference was with BT-SSMI (less than 0.1 million km2), whereas the largest difference was with NT1-SSMI (up to 1.5 million km2). In comparisons of sea ice extent, BT-AMSR, NT1-SSMI, and NT2-AMSR estimates were consistent with that of DPR-AMSR and were within ±0.5 million km2. However, differences exceeded 0.5 million km2 between DPR-AMSR and the other data sets. When ship-based visual observation (OBS) values ranged from 85% to 100%, the difference between DPR-AMSR and OBS was less than 1%. There were relatively large differences between DPR-AMSR and OBS when OBS values were less than 85% or were recorded during the summer, although those differences were also within 10%.

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends.

Sea ice detection from SSMI data based on an improved U-Net convolution neural network

ABSTRACT It is of great significance to accurately obtain the polar sea ice for the study of global hydrosphere and polar climate. The U-Net convolutional neural network (CNN) can achieve end-to-end pixel-level semantic segmentation, and it can learn comprehensive image information as much as possible through the training process. The vertical polarization at 37 GHz and 19 GHz of Special Sensor Microwave/Imager (SSM/I) is used as the input of the improved U-Net CNN, and the improved U-Net CNN is proposed to detect the sea ice. In order to improve the accuracy of sea ice detection, anew feature fusion structure based on the improved U-Net CNN is constructed, which further refines the features of different semantic levels. By using batch normalization (BN), dropout regularization and modifying the loss function to improve the U-Net convolutional neural network, the accuracy of sea ice detection is improved. The results were compared with the NASA Team algorithm, which showed that the improved U-Net CNN is afeasible solution. The sea ice obtained from MODIS data is real result, and our comparison with NASA team algorithm and U-Net CNN showed that the modified U-Net CNN improves the accuracy of sea ice detection to 75%. Therefore, the improved U-Net CNN significantly improves the accuracy of sea ice detection. The improved U-Net CNN had the ability to combine multiple nonlinear transformation functions to solve the problems of sea ice identification, which improved the accuracy and robustness of sea ice detection.

Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Multihop Trust Evaluation Using Memory Integrity in Wireless Sensor Networks

Research efforts in trust evaluation has evolved to provide security in Wireless Sensor Networks (WSNs), while being dependent on external parameters and network topology. Existing node level trust evaluation in WSNs evaluate trust over 1-hop assuring trustworthiness of an immediate node. This paper proposes trust evaluation using the intrinsic property of a node memory over a multihop scenario which also evaluates the route. The work proposes a multihop trust evaluation protocol using TEAM and TEAP algorithms. Two proposed trust evaluation models each are proposed using normative and empirical methods in multihop algorithms. The proposed methodology establishes a trusted destination node along with trusted nodes in the route. The efficacy of the proposed work and its implementation is demonstrated using extensive experiments and the results illustrate consistency and resilience against node memory tampering.

Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

Standardizing Preoperative Evaluation for Pediatric Central Venous Access: A Care Algorithm to Improve Safety.

Central vascular access device (CVAD) placement is a common procedure in children. When selecting a CVAD, available evidence and specified indications should be used to choose the device that best supports the patient's treatment and carries the lowest risks. A multidisciplinary team developed a care algorithm to standardize preoperative screening before pediatric CVAD placement, with 3 major parts: CVAD selection, patient risk stratification, and preoperative evaluation. Using a stepwise approach of provider education and incorporation into the electronic health record, the team achieved 82% stratification among inpatients. The team's algorithm integrates the existing literature and recommendations for safe and effective CVAD placement.

INTER-COMPARISONS AMONG PASSIVE MICROWAVE SEA ICE CONCENTRATION PRODUCTS FROM FY-3D MWRI AND AMSR2

Abstract. Passive microwave (PM) sensors on satellite can monitor sea ice distribution with their strengths of daylight- and weather-independent observations. Microwave Radiation Imager (MWRI) sensor aboard on the Chinese FengYun-3D (FY-3D) satellites was launched in 2017 and provides continuous observation for Arctic sea ice since then. In this study, sea ice concentration (SIC) product is derived from brightness temperature (TB) data of MWRI, based on an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm. Our product is inter-compared with a published MWRI SIC product by the Enhanced NASA Team (NT2) algorithm, and three Advanced Microwave Scanning Radiometer 2 (AMSR2) SIC products by the ASI, Bootstrap (BST) and NT2 algorithm. Results show that MWRI SIC are generally higher than AMSR2 SIC and the median of monthly SIC differences are larger in summer. Regional analysis indicates that the smaller differences between AMSR2 SIC and MWRI-ASI SIC occur in the higher SIC areas, and the biases are within ±5% in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Canadian Archipelago Sea and Central Arctic Sea. There is the smallest SIC difference in the Central Arctic Sea with the biases of −0.77%, −0.60%, and 0.19% for AMSR2-ASI, AMSR2-BST and AMSR2-NT2, respectively. The trends of MWRI and AMSR2 sea ice extent and sea ice area are consistent with correlation coefficients all greater than 0.997. Besides, mean SIC, sea ice extent and sea ice area of MWRI-ASI are closer to those of AMSR2 than those of MWRI-NT2.

Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals

Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.

Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

Ice Concentration Estimation Method with Satellite based Microwave Radiometer by Means of Inversion Theory

Ice concentration estimation method with satellite-based microwave radiometer by means of inversion theory is proposed. Through experiments, it is found that the proposed methods are superior to the existing methods, the NASA Team algorithm and the Comiso's Bootstrap algorithm with up to 45% of improvement on ice concentration estimation accuracy based on the simulation study. Also 1.5 to 2.1% of improvement was achieved for the proposed method compared to the NASA Team and Comiso's Bootstrap algorithms for the actual The Special Sensor Microwave Imager (SSM/I) data of Okhotsk using Japanese Earth Resources Satellite: JERS-1/Synthetic Aperture Radar: SAR data as a truth data for estimating ice concentration.

Comparison of Satellite Microwave and Visual Shipborne Data on Sea Ice Concentration

In this paper we compare the sea ice concentration data obtained by satellite microwave radiometry using the NASA Team, ASI, and VASIA2 algorithms and by special visual ship observations. A database of visual observations of the sea ice concentration obtained during 15 expeditions in the Arctic was used. The comparison for various concentration gradations (very open, open, close, very close, and compact ice) was carried out separately for summer and winter periods. On average, during the summer period, the NASA Team algorithm underestimates the sea ice concentration by 1 point, while the ASI and VASIA2 algorithms underestimate by 0.5 points when compared to ship observations. All three algorithms overestimate the total concentration in the zones of very open ice and underestimate it in the zones of close, very close, and compact ice. The maximal average errors are characteristic of open ice that is most often observed in ice edge zones. In winter, the average error for all algorithms is no more than 1 point when compared with ship observations. However, in very open and close, the average error in winter is significantly higher than that in summer. To assess the effect of melting processes on the concentration values obtained by satellite microwave radiometry, we used ship data on ice melt stage. The average error at the maximal area of ​​melt ponds on the ice surface reaches –2.9 points for the NASA Team algorithm, –2.8 points for the ASI algorithm, and –5.0 points for the VASIA2 algorithm. The results will be useful for determining the extent of ice-cover degradation in the Arctic Ocean observed in recent years.

Heuristic-Guided Solution Search Through a Two-Tiered Design Grammar

Abstract Grammar-based design is typically a gradual process; incremental design changes are performed until a problem statement has been satisfied. While they offer an effective means for searching a design space, standard grammars risk being computationally costly because of the iteration required, and the larger a given grammar the broader the search required. This paper proposes a two-tiered design grammar that enhances the computational design generation with generalized heuristics to provide a way to more efficiently search a design space. Specifically, this two-tiered grammar captures a combination of heuristic-based strategic actions (often observed in human designers) and smaller-scale modifications (common in traditional grammars). Rules in the higher tier are abstract and applicable across multiple design domains. Through associated guiding heuristics, these macrorules are translated down into a sequence of domain-specific, lower-tier microrules. This grammar is evaluated through an implementation within an agent-based simulated annealing team algorithm in which agents iteratively select actions from either the higher tier or the lower tier. This algorithm is used in two applications: truss generation, which is commonly used for testing engineering design methods, and wave energy converter design generation, which is currently a relevant research area in sustainable energy production. Comparisons are made between designs generated using only lower-tier rules and those generated using only higher-tier rules. Further tests demonstrate the efficacy of applying a combination of both lower-tier and higher-tier rules.

Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

Variability in Antarctic sea ice from 1998 to 2017.

This study was based on the daily sea ice concentration data from the NASA Team algorithm from 1998 to 2017. The Antarctic sea ice was analyzed from the total sea ice area (SIA), first-year ice area, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20years. The results showed that the total SIA increased by 0.0087 × 106km2year-1 (+ 2.08% dec-1) between 1998 and 2017. The multiyear ice area increased by 0.0141 × 106km2year-1 from 1998 to 2017. The first-year ice area decreased by - 0.0058 × 106km2year-1 between 1998 and 2017. On a spatial scale, the entire Antarctic was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The total sea ice and multiyear ice areas showed a decreasing trend in WA. However, the total SIA and multiyear ice area all showed an increasing trend in EA. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA.

İlişkisel Verilere Dayalı Bilişim Sistemleri Araştırmalarında Geçerlik Konuları

This research adopts a complex system approach to linked data, which has a trace aspect and to examine validation issues in linked data driven IS research. Thereby a relevant question arises: What are the validity issues in the overall network analysis process applied on such linked data? This research argues that validity issues are vital to research in linked data and requires a complex system approach so that true value of linked data can be discerned and applicable to the real-world cases. Particular emphasis is placed on the validation issues in empirical research on linked data concerned with the educational system. This paper should be considered as a contribution to the efforts of those who are struggling with the validity issues in SNA. The intention of the work is to build a checklist that can be used to check the validity of the data, methods, and algorithms for transdisciplinary research teams who utilize theory of networks in general and SNA in particular in a particular domain, which is an educational system for the focus of this research. The findings may help the school administrators, instructors and student advisors in the decision making processes.