cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/cu.the.2011.1506
title: การพัฒนายุทธศาสตร์การพัฒนาคุณธรรมสำหรับนักเรียนนายร้อยตำรวจ

description: พัฒนายุทธศาสตร์การพัฒนาคุณธรรมสำหรับนักเรียนนายร้อยตำรวจ โดยใช้การวิจัยเชิงพรรณนาและวิธีวิจัยแบบผสมผสาน (Mixed methods) เก็บรวบรวมข้อมูลเชิงคุณภาพโดยการสัมภาษณ์ผู้บริหาร และการสนทนากลุ่มโดยผู้เชี่ยวชาญ เครื่องมือที่ใช้ คือ แบบสัมภาษณ์และแบบสนทนากลุ่ม วิเคราะห์ข้อมูลด้วยวิธีวิเคราะห์เชิงเนื้อหา เก็บรวบรวมข้อมูลเชิงปริมาณโดยการใช้แบบสำรวจความคิดเห็นของประชาชนที่เข้ารับบริการในสถานีตำรวจ จำนวน 405 คน แบบสอบถามบุคลากรของโรงเรียนนายร้อยตำรวจ จำนวน 227 คน วิเคราะห์ข้อมูลโดยใช้การวิเคราะห์ความถี่ ร้อยละ ค่าเฉลี่ย และส่วนเบี่ยงเบนมาตรฐาน งานวิจัยนี้ใช้วิธีกำหนดยุทธศาสตร์จากตาราง SWOT matrix โดยผู้เชี่ยวชาญด้านการวางแผนยุทธศาสตร์ ด้านการบริหารการศึกษา ด้านการพัฒนาคุณธรรมและผู้ที่เกี่ยวข้องโดยตรงในการนำยุทธศาสตร์ไปปฏิบัติ จำนวน 6 คน ยุทธศาสตร์ที่ได้จากการศึกษาครั้งนี้มีการประเมินความเหมาะสม และความเป็นไปได้โดยผู้ทรงคุณวุฒิ และผู้ที่นำยุทธศาสตร์ไปใช้จำนวน 1 5 คน ผลการวิจัยพบว่า ยุทธศาสตร์การพัฒนาคุณธรรมสำหรับนักเรียนนายร้อยตำรวจมี 6 ยุทธศาสตร์ ได้แก่ 1) ยุทธศาสตร์การสร้างตำรวจต้นแบบตามความคาดหวังของสังคม 2) ยุทธศาสตร์การส่งเสริมกิจกรรมประจำวันของนักเรียนนายร้อยตำรวจให้สอดคล้องตามหลักเศรษฐกิจพอเพียง 3) ยุทธศาสตร์การส่งเสริมผลักดันองค์ความรู้ใหม่สู่การปฏิบัติหน้าที่เพื่อประชาชน 4) ยุทธศาสตร์การระดมสรรพกำลังเพื่อเสริมสร้างหลักธรรมาภิบาลในการบริหารจัดการโรงเรียนนายร้อยตำรวจ 5) ยุทธศาสตร์การบริหารทรัพยากรของโรงเรียนนายร้อยตำรวจให้เกิดประสิทธิภาพประสิทธิผล และ 6) ยุทธศาสตร์การพัฒนาเครือข่ายความร่วมมือ ระหว่างโรงเรียนกับชุมชนสังคมอย่างเหมาะสม
DOI: 10.14457/cu.the.2011.1507
title: อาร์เอ็นเอสายคู่ใน Trichoderma sp. สายพันธุ์ที่แยกได้ในประเทศไทย

description: ผลการตรวจสอบ double-stranded RNA (dsRNA) ในรา Trichoderma จำนวน 96 ไอโซเลต ที่คัดแยกจาก 13 จังหวัดในประเทศไทย พบ dsRNA ใน 7 ไอโซเลต (7%) ได้แก่ Trichoderma sp. Cbi3.1.1 T. harzianum Cbi3.2.2 T. reesei Cbi3.3.1 Trichoderma sp. Cpn1.1.1 Trichoderma sp. Pbn1.1.5 T. virens Plk1.1.9 และ Trichoderma sp. Ubn1.1.3 ลักษณะ dsRNA ที่พบในราแต่ละไอโซเลตมีจำนวนตั้งแต่ 1-10 แถบ และมีขนาด โดยประมาณ 1.25–21.9 kb จากนั้น ได้เลือก dsRNA จาก T. reesei Cbi3.3.1 ซึ่งมีจำนวน 3 แถบ ขนาดประมาณ 2.06 – 2.26 kb ไปสังเคราะห์ cDNA โคลน และศึกษาลำดับนิวคลีโอ ไทด์ พบว่าลำดับนิวคลีโอไทด์บางส่วนของ dsRNA จาก cDNA โคลน c88 c101 และ c171 สามารถถอดรหัสเป็นโปรตีนซึ่งมีความคล้ายคลึงกับ putative RNA dependent RNA polymerases และ putative capsid proteins ของไวรัสในสกุล Partitivirus ผลการวิเคราะห์ ความสัมพันธ์ทางวิวัฒนาการยืนยันว่า dsRNA จาก T. reesei Cbi3.3.1 มีความใกล้ชิดกับ ไวรัสสกุล Partitivirus การศึกษาเปรียบเทียบระหว่างโคโลนีเซลล์เดี่ยวที่มีและไม่มี dsRNA ของ T. reesei Cbi3.3.1 พบว่า dsRNA มีผลต่อการเติบโตที่แตกต่างกันเมื่อทำการเลี้ยงในอาหาร Corn meal dextrose agar ที่อุณหภูมิ 30°C และ 40°C และพบการสร้างสปอร์ที่ ต่างกันภายหลังการเลี้ยงเป็นเวลา 14 วัน ในขณะที่การเลี้ยงในอาหาร Synthetic low nutrient agar พบความแตกต่างที่อุณหภูมิ 15°C เท่านั้น สำหรับการผลิตเอนไซม์ endoglucanase ไม่พบความแตกต่างของค่าเฉลี่ยของแอคติวิตีและแอคติวิตีจำเพาะของ เอนไซม์ในโคโลนีทั้งสองกลุ่ม แต่พบว่ากลุ่มของโคโลนีที่ไม่มี dsRNA มีค่าเฉลี่ยของปริมาณ โปรตีนใน crude enzyme ที่สูงกว่าอย่างมีนัยสำคัญ
DOI: 10.14457/cu.the.2011.1508
title: การคำนวณบริเวณสัมผัสอิสระที่ดีที่สุดบนวัตถุหลายเหลี่ยมในสองและสามมิติด้วยนิ้วจับสองนิ้ว

description: นำเสนอวิธีคำนวณหาบริเวณสัมผัสอิสระที่ดีที่สุดบนวัตถุหลายเหลี่ยม ในสองและสามมิติด้วยนิ้วจับสองนิ้ว ในกรณีสองมิติ จะใช้วิธีเปลี่ยนปัญหาไปบนมิติของบริเวณที่จับติด และคำนวณหาสี่เหลี่ยมจัตุรัสที่ใหญ่ที่สุดที่อยู่บนมิติดังกล่าว โดยใช้ L∞ voronoi diagram เป็นตัวหาคำตอบ ในกรณีสามมิติจะใช้การเปลี่ยนปัญหาให้อยู่ในรูปของกราฟ ความสัมพันธ์ของความใกล้กันของแต่ละหน้าของวัตถุสามมิติ และคุณสมบัติฟอร์สโคลสเชอร์ของแต่ละคู่หน้า และใช้การประมวลผลแบบขนาดโดยการ์ดแสดงผลเป็นตัวคำนวณหาบริเวณสัมผัสอิสระที่ดีที่สุด
DOI: 10.21227/2fj8-cz17
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.
DOI: 10.21227/gaa9-ks10
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.
DOI: 10.21227/pwsh-cs05
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.
DOI: 10.7910/dvn/qlmwhr/1oskcd
title: 10lux_LDLL_W342Y_Rep3_Fly10.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/8outch
title: 1000lux_LDLL_W397Y_Rep1_Fly10.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/dmahyv
title: 10lux_LDLL_W342Y_Rep3_Fly11.txt

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DOI: 10.7910/dvn/qlmwhr/enmter
title: 1000lux_LDLL_W397Y_Rep1_Fly11.txt

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DOI: 10.7910/dvn/qlmwhr/fenqlg
title: 1000lux_LDLL_W397Y_Rep1_Fly13.txt

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DOI: 10.7910/dvn/qlmwhr/fwo3sh
title: 10lux_LDLL_W342Y_Rep3_Fly13.txt

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DOI: 10.7910/dvn/qlmwhr/jsokzi
title: 1000lux_LDLL_W397Y_Rep1_Fly17.txt

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DOI: 10.7910/dvn/qlmwhr/jy8mgf
title: 1000lux_LDLL_W397Y_Rep1_Fly15.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/lk4br3
title: 10lux_LDLL_W342Y_Rep3_Fly08.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/nlvpac
title: 10lux_LDLL_W342Y_Rep3_Fly09.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/qi2cbm
title: 1000lux_LDLL_W397Y_Rep1_Fly14.txt

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DOI: 10.7910/dvn/qlmwhr/qvuvoo
title: 1000lux_LDLL_W397Y_Rep1_Fly18.txt

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DOI: 10.7910/dvn/qlmwhr/r9bfl1
title: 1000lux_LDLL_W397Y_Rep1_Fly12.txt

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DOI: 10.7910/dvn/qlmwhr/rjqjka
title: 10lux_LDLL_W342Y_Rep3_Fly07.txt

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DOI: 10.7910/dvn/qlmwhr/s5jxhd
title: 10lux_LDLL_W342Y_Rep3_Fly06.txt

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DOI: 10.7910/dvn/qlmwhr/uiqkf2
title: 10lux_LDLL_W342Y_Rep3_Fly12.txt

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DOI: 10.7910/dvn/qlmwhr/wwu7tc
title: 10lux_LDLL_W342Y_Rep3_Fly14.txt

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DOI: 10.7910/dvn/qlmwhr/ycpbmx
title: 1000lux_LDLL_W397Y_Rep1_Fly16.txt

description: :unav
DOI: 10.21227/5s7w-da62
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.
DOI: 10.21227/6x08-j759
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.
DOI: 10.21227/zfgg-wb27
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.
DOI: 10.25946/5da9298201a73
title: Project ISP005-2017: Social, cultural (Sense of Place) and economic indicator scores and grades for the Gladstone Healthy Harbour Partnership 2017 Report Card

description: The Gladstone Healthy Harbour Partnership (GHHP) was established to provide support for improved decision making in the environmental management of Gladstone Harbour. The GHHP vision is that ‘Gladstone has a healthy, accessible, working harbour’. To assist in this decision making, the GHHP has developed an annual report card on the health of the harbour. In 2014, the GHHP decided upon a framework for the Gladstone Harbour Report Card. This framework outlined the four components of harbour health: environmental, social, cultural and economic aspects. The report card was piloted in 2014 and there have now been three years of reporting (2014-16). The project specifications for this proposal require the contractor to follow previously documented methods (data collection and analysis) developed by Pascoe et al. 2014. Data analysis has now been fully automated and all minor adjustments / corrections have been made to the R-script. In this project proposal we retain and apply the expertise of the same team of CQU researchers who delivered the scores and grades for the same components in the 2016 report card. This ensures continuity and knowledge transfer across the reporting periods. Further information is available from http://ghhp.org.au/report-cards/2017 .
DOI: 10.7910/dvn/qlmwhr/49d1mp
title: 1000lux_LDLL_W397Y_Rep1_Fly24.txt

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DOI: 10.7910/dvn/qlmwhr/511ujx
title: 10lux_LDLL_W342Y_Rep3_Fly16.txt

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DOI: 10.7910/dvn/qlmwhr/9bm2hs
title: 10lux_LDLL_W342Y_Rep3_Fly21.txt

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DOI: 10.7910/dvn/qlmwhr/ewyp9k
title: 10lux_LDLL_W342Y_Rep3_Fly20.txt

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DOI: 10.7910/dvn/qlmwhr/fms8i9
title: 1000lux_LDLL_W397Y_Rep1_Fly21.txt

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DOI: 10.7910/dvn/qlmwhr/h29hxc
title: 10lux_LDLL_W342Y_Rep3_Fly17.txt

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DOI: 10.7910/dvn/qlmwhr/kfnhl9
title: 10lux_LDLL_W342Y_Rep3_Fly19.txt

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DOI: 10.7910/dvn/qlmwhr/knksry
title: 1000lux_LDLL_W397Y_Rep1_Fly26.txt

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DOI: 10.7910/dvn/qlmwhr/ojskkx
title: 1000lux_LDLL_W397Y_Rep1_Fly25.txt

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DOI: 10.7910/dvn/qlmwhr/s9lnqf
title: 10lux_LDLL_W342Y_Rep3_Fly18.txt

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DOI: 10.7910/dvn/qlmwhr/shkxwi
title: 1000lux_LDLL_W397Y_Rep1_Fly19.txt

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DOI: 10.7910/dvn/qlmwhr/sn4xbr
title: 1000lux_LDLL_W397Y_Rep1_Fly27.txt

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DOI: 10.7910/dvn/qlmwhr/trhrpq
title: 1000lux_LDLL_W397Y_Rep1_Fly20.txt

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DOI: 10.7910/dvn/qlmwhr/ujmojp
title: 10lux_LDLL_W342Y_Rep3_Fly15.txt

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DOI: 10.7910/dvn/qlmwhr/uq4z0n
title: 1000lux_LDLL_W397Y_Rep1_Fly22.txt

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DOI: 10.7910/dvn/qlmwhr/vyiuhh
title: 1000lux_LDLL_W397Y_Rep1_Fly23.txt

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DOI: 10.7910/dvn/qlmwhr/xgspaf
title: 10lux_LDLL_W342Y_Rep3_Fly23.txt

description: :unav
DOI: 10.7910/dvn/qlmwhr/xj74rw
title: 10lux_LDLL_W342Y_Rep3_Fly22.txt

description: :unav
DOI: 10.17632/6wxzx2p3pn.1
title: Data for: Significant impacts of pelagic clay on average chemical composition of subducting sediments: New insights from discovery of extremely rare-earth elements and yttrium-rich mud at Ocean Drilling Program Site 1149 in the western North Pacific Ocean

description: Table 1. Results of the bulk chemical analyses of samples obtained from ODP Site 1149. Table 2. Comparison of the average compositions between the previous study and this study.
DOI: 10.21227/0hec-8m07
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.
DOI: 10.21227/gdnb-p692
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras.