Creative works of Nirmal Varma

Hindi

Public and student awareness science: recreational science as a catalyst

When discussing the traditional and new missions of higher education (1996 Report to UNESCO of the International Commission on Education for the 21st Century) Jacques Delors stated that "Excessive attraction to social sciences has broken equilibrium of available graduates for workforce, thus causing doubts of graduates and employers on the quality of knowledge provided by higher education". Likewise, when discussing the progress of science and technology, the 1998 UNESCO World Conference on Higher Education concluded that "Another challenge concerts the latest advancements of Science, the sine qua non of sustainable development"; and that “with Information Technology, the unavoidable invasion of virtual reality has increased the distance between industrial and developing countries". Recreational Science has a long tradition all over the Educational World; it aims to show the basic aspects of Science, aims to entertain, and aims to induce thinking. Until a few years ago, this field of knowledge consisted of a few books, a few kits and other classical (yet innovative) ways to popularize the knowledge of Nature and the laws governing it. In Spain, the interest for recreational science has increased in the last years. First, new recreational books are being published and found in bookstores. Second the number of Science-related museums and exhibits is increasing. And third, new television shows are produced and new short science-based, superficial sketches are found in variety programs. However, actual programs in Spanish television dealing seriously with Science are scarce. Recreational Science, especially that related to physical phenomena like light or motion, is generally found at Science Museums because special equipment is required. On the contrary, Science related mathematics, quizzes and puzzles use to gather into books, e.g. the extensive collections by Martin Gardner. However, lately Science podcasts have entered the field of science communication. Not only traditional science journals and television channels are providing audio and video podcasts, but new websites deal exclusively with science podcasts, in particular on Recreational Science. In this communication we discuss the above mentioned trends and show our experience in the last two years in participating at Science Fairs and university-sponsored events to attract students to science and technology careers. We show a combination of real examples (e.g., mathemagic), imagination, use of information technology, and use of social networks. We present as well an experience on designing a computational, interactive tool to promote chemistry among high school, prospective students using computers ("Dancing with Bionanomolecules"). Like the concepts related to Web 2.0, it has been already proposed that a new framework for communication of science is emerging, i.e., Science Communication 2.0, where people and institutions develop new innovative ways to explain science topics to diverse publics – and where Recreational Science is likely to play a leading role

Modelling: promoting creativity whilst forging links between science education and design and technology education

Educational reforms in many countries currently call for the development of knowledge-based societies. In particular, emphasis is placed on the promotion of creativity, especially in the areas of science education and of design and technology education. In this paper, perceptions of the nature of creativity and of the conditions for its realization are discussed. The notion of modelling as a creative act is outlined and the scope for using modelling as a bridge between science education and design and technology education explored. A model for the creative act of modelling is proposed and its major aspects elaborated upon. Finally, strategies for forging links between the two subjects are outlined.

Talent on the move: creative human capital migration patterns in UK

The last decade has seen an increasing number of contributions, from both academics and policy makers, focusing on the role of higher education in developing human capital (Charles, 2003; Cramphorn & Woodlhouse, 1999; Preston & Hammond, 2006) and hence contributing to local and regional growth (Faggian & McCann, 2006; Mathur, 1999; Moretti, 2004). Within this broader literature, the role played by more ‘scientific’ types of human capital, such as STEM (science, technology, engineering, and mathematics) graduates and science parks (Bozeman, Dietz, & Gaughan, 2001; Linderlöf & Löfsten, 2004; Löfsten & Lindelöf, 2005), has also been explored. Little attention has been paid so far, to the role played by more ‘creative’ types of human capital. This chapter aims at filling this gap, in light of the central role that the term ‘creative’ took in policy and academic discourses in the UK (Comunian & Faggian, 2011; Comunian & Gilmore, 2015; DCMS, 2006; Powell, 2007; Universities UK, 2010).

Creative commons: A convergence model between the ideal of commons and the possibilities of creation in contemporary times, opposed to copyright impediments

The contemporary individual finds on the Internet and especially on the Web facilitating conditions to build a basic infrastructure based on the concept of commons. He also finds favorable conditions which allow him to collaborate and share resources for the creation, use, reuse, access and dissemination of information. However, he also faces obstacles such as Copyright (Law 9610/98 in Brazil). An alternative is Creative Commons which not only allows the elaboration, use and dissemination of information under legal conditions but also function as a facilitator for the development of informational commons. This paper deals with this scenario.

Creative Commons: a convergence model between the ideal of commons and the possibilities of creation in contemporary times, opposed to copyright impediments

The contemporary individual finds on the Internet and especially on the Web facilitating conditions to build a basic infrastructure based on the concept of commons. He also finds favorable conditions which allow him to collaborate and share resources for the creation, use, reuse, access and dissemination of information. However, he also faces obstacles such as Copyright (Law 9610/98 in Brazil). An alternative is Creative Commons which not only allows the elaboration, use and dissemination of information under legal conditions but also function as a facilitator for the development of informational commons. This paper deals with this scenario.

Hollows of a science of consciousness?

I consider Nixon’s essay a well thought discussion of the possibility of a genuine science of consciousness. Most of the sections are worth discussing, but to find the main message it may be necessary to read between the lines. The good news is that he does not present true impossibilities for this science, but his discussion leads to the (sound) conclusion that it would have to account for many unconscious factors that make us creative and human.

Abundance of mesozooplankton in the western part of the Black Sea in 1995 during the Cooperative Marine Science Program for the Black Sea (CoMSBlack)

The "CoMSBlack-95" dataset is based on samples collected in the summer of 1995. The whole dataset is composed of 81 samples (28 stations) with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36 cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Mesozooplankton abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov and Lyudmila Kamburska using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov and Lyudmila Kamburska using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972).

Biomass of mesozooplankton in the western part of the Black Sea in 1997 during the Cooperative Marine Science Program for the Black Sea (CoMSBlack)

The "15BO1997001" dataset is based on samples collected in the spring of 1997. The whole dataset is composed of 66 samples (from 27 stations of National Monitoring Sampling Grid) with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. The collected material was analysed using the method of Dimov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972 ). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. The collected material was analysed using the method of Dimov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972 ). The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m3. WW were converted to DW by equation DW=0.16*WW (Vinogradov ME, Sushkina EA, 1987).

Web-based Biodiversity Citizen Science Database (assembled 2012)

The collective impact of humans on biodiversity rivals mass extinction events defining Earth's history, but does our large population also present opportunities to document and contend with this crisis? We provide the first quantitative review of biodiversity-related citizen science to determine whether data collected by these projects can be, and are currently being, effectively used in biodiversity research. We find strong evidence of the potential of citizen science: within projects we sampled (n = 388), ~1.3 million volunteers participate, contributing up to US Dollar 2.5 billion in-kind annually. These projects exceed most federally-funded studies in spatial and temporal extent, and collectively they sample a breadth of taxonomic diversity. However, only 12% of the 388 projects surveyed obviously provide data to peer-reviewed scientific articles, despite the fact that a third of these projects have verifiable, standardized data that are accessible online. Factors influencing publication included project spatial scale and longevity and having publically available data, as well as one measure of scientific rigor (taxonomic identification training). Because of the low rate at which citizen science data reach publication, the large and growing citizen science movement is likely only realizing a small portion of its potential impact on the scientific research community. Strengthening connections between professional and non-professional participants in the scientific process will enable this large data resource to be better harnessed to understand and address global change impacts on biodiversity.

Biomass of mesozooplankton in the western part of the Black Sea in 1992 during the Cooperative Marine Science Program for the Black Sea (CoMSBlack)

The "CoMSBlack92" dataset is based on samples collected in the summer of 1992 along the Bulgarian coast including coastal and open sea areas. The whole dataset is composed of 79 samples (28 stations) with data of zooplankton species composition, abundance and biomass. Sampling for zooplankton was performed from bottom up to the surface at standard depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 ?m. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling volume was estimated by multiplying the mouth area with the wire length. The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972 ). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m**3.