cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.793833
title: Emory University Research Data Management Faculty Survey

description: In the fall of 2012, the Emory University Libraries, in cooperation with the Emory University Office of Institutional Research, Planning, and Effectiveness, sent an email invitation to all employees with faculty status as classified by Human Resources to voluntarily complete a brief online survey of their research data management practices and perspectives. The survey was comprised of 13 possible questions and was administered using Qualtrics software. It was open for 4 weeks, and three email reminders were sent at 1-week intervals. Survey respondents were allowed to move forward in the survey without requiring an answer to each question. For multiple choice questions, respondents were given the choice of selecting ‘other’ and writing in a text response. Over 400 faculty members from a wide range of schools and colleges across campus responded to the survey, with over 350 faculty stating that they generate some type of research data (e.g., spreadsheets, text, images, videos, audio files, instrument files, photographs, physical samples/specimens, etc.).
DOI: 10.6084/m9.figshare.793839.v2
title: Supplemental Item 2 - all raw data and traits.

description: Supplemental Item 2. All raw data and traits (workings given as comments in the excel file) used in the morphometric analysis and figures.
DOI: 10.6084/m9.figshare.793833.v1
title: Emory University Research Data Management Faculty Survey

description: In the fall of 2012, the Emory University Libraries, in cooperation with the Emory University Office of Institutional Research, Planning, and Effectiveness, sent an email invitation to all employees with faculty status as classified by Human Resources to voluntarily complete a brief online survey of their research data management practices and perspectives. The survey was comprised of 13 possible questions and was administered using Qualtrics software. It was open for 4 weeks, and three email reminders were sent at 1-week intervals. Survey respondents were allowed to move forward in the survey without requiring an answer to each question. For multiple choice questions, respondents were given the choice of selecting ‘other’ and writing in a text response. Over 400 faculty members from a wide range of schools and colleges across campus responded to the survey, with over 350 faculty stating that they generate some type of research data (e.g., spreadsheets, text, images, videos, audio files, instrument files, photographs, physical samples/specimens, etc.).
DOI: 10.6084/m9.figshare.793840.v2
title: Evolution and speciation in the Eocene planktonic foraminifer Turborotalia

description: Supplementary materials to accompany Pearson & Ezard (2014) Evolution and speciation in the Eocene planktonic foraminifer <em>Turborotalia. Paleobiology. </em><strong>40</strong>(1), 130-143. doi: 10.1666/13004
DOI: 10.6084/m9.figshare.793828
title: Periphyton biomass spatial patterns and species abundances

description: Images of periphyton's biomass colonization in artificial substrata. The experiment was described in [1] but these images were not analyzed in the paper and not included in the data uploaded to dryad [2]. The original hypothesis was that the growth of microalgae in artificial substrata have a two dimensional multifractal structure. I include measurements of environmental variables and nutrients not analyzed in the paper. We used this software: https://github.com/lsaravia/tiff2biom https://github.com/lsaravia/mfsba References 1. Saravia LA, Giorgi A, Momo F (2012) Multifractal growth in periphyton communities. Oikos 121: 1810–1820. doi:10.1111/j.1600-0706.2011.20423.x. 2. Saravia LA, Giorgi A, Momo F (2012) Data from: Multifractal growth in periphyton communities. Oikos. doi:10.5061/dryad.61cj4.
DOI: 10.6084/m9.figshare.793816
title: White Paper on DOE-HEP Accelerator Modeling Science Activities

description: Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within DOE-HEP, and (b) increasing the community-wide coordination and integration of code development.
DOI: 10.6084/m9.figshare.791705
title: Computational Structure Reassignment of Laurefurenynes A and B

description: The originally assigned stereostructures of laurefurenynes A and B have been reassigned on the basis of DFT calculations of NMR chemical shifts. The deposited files are outputs from geometry optimizations and subsequent calculations of shielding tensors in CPCM, for each of the putative diastereomers. The correct structure is number 6, as described in the paper (http://onlinelibrary.wiley.com/doi/10.1002/chem.201302349/pdf) The data was generated as follows: For each distereomer a 10,000 step Monte Carlo Multiple Minimum (MCMM) conformational search was performed using the MMFF force field. Resulting molecular mechanics geometries within 10 kJ/mol of the global energy minimum were reoptimized with Gaussian09 at the wB97XD/6-31G(d) level with CPCM chloroform. Following these DFT optimizations duplicate conformers were removed to give 2733 conformations across all diastereomers (present as Gaussian formatted output files). For each DFT optimized conformer, shielding tensors were computed at the GIAO-mPW1PW91/6-311G(d,p) level of theory with CPCM chloroform solvation (present as Gaussian formatted output files). The conformational ensemble average 13C and 1H shielding tensor was computed from Boltzmann factors at 298K using the electronic energies (present as text files).
DOI: 10.6084/m9.figshare.791666
title: SNPs from Ash Dieback samples

description: All SNPs in plot of SNPs http://figshare.com/articles/SNP_Patterns_in_Ash_Dieback_fungus_genome_from_UK_France_and_Japan_samples/791641 Here
DOI: 10.6084/m9.figshare.791666.v1
title: SNPs from Ash Dieback samples

description: All SNPs in plot of SNPs http://figshare.com/articles/SNP_Patterns_in_Ash_Dieback_fungus_genome_from_UK_France_and_Japan_samples/791641 Here
DOI: 10.6084/m9.figshare.793816.v1
title: White Paper on DOE-HEP Accelerator Modeling Science Activities

description: Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within DOE-HEP, and (b) increasing the community-wide coordination and integration of code development.
DOI: 10.6084/m9.figshare.791705.v6
title: Computational Structure Reassignment of Laurefurenynes A and B

description: The originally assigned stereostructures of laurefurenynes A and B have been reassigned on the basis of DFT calculations of NMR chemical shifts. The deposited files are outputs from geometry optimizations and subsequent calculations of shielding tensors in CPCM, for each of the putative diastereomers. The correct structure is number 6, as described in the paper (http://onlinelibrary.wiley.com/doi/10.1002/chem.201302349/pdf) The data was generated as follows: For each distereomer a 10,000 step Monte Carlo Multiple Minimum (MCMM) conformational search was performed using the MMFF force field. Resulting molecular mechanics geometries within 10 kJ/mol of the global energy minimum were reoptimized with Gaussian09 at the wB97XD/6-31G(d) level with CPCM chloroform. Following these DFT optimizations duplicate conformers were removed to give 2733 conformations across all diastereomers (present as Gaussian formatted output files). For each DFT optimized conformer, shielding tensors were computed at the GIAO-mPW1PW91/6-311G(d,p) level of theory with CPCM chloroform solvation (present as Gaussian formatted output files). The conformational ensemble average 13C and 1H shielding tensor was computed from Boltzmann factors at 298K using the electronic energies (present as text files).
DOI: 10.6084/m9.figshare.791639
title: Gaussian Job Archive for C27H31F6FeN7O6S2(5)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.791639.v1
title: Gaussian Job Archive for C27H31F6FeN7O6S2(5)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.791705.v7
title: Computational Structure Reassignment of Laurefurenynes A and B

description: The originally assigned stereostructures of laurefurenynes A and B have been reassigned on the basis of DFT calculations of NMR chemical shifts. The deposited files are outputs from geometry optimizations and subsequent calculations of shielding tensors in CPCM, for each of the putative diastereomers. The correct structure is number 6, as described in the paper (http://onlinelibrary.wiley.com/doi/10.1002/chem.201302349/pdf) The data was generated as follows: For each distereomer a 10,000 step Monte Carlo Multiple Minimum (MCMM) conformational search was performed using the MMFF force field. Resulting molecular mechanics geometries within 10 kJ/mol of the global energy minimum were reoptimized with Gaussian09 at the wB97XD/6-31G(d) level with CPCM chloroform. Following these DFT optimizations duplicate conformers were removed to give 2733 conformations across all diastereomers (present as Gaussian formatted output files). For each DFT optimized conformer, shielding tensors were computed at the GIAO-mPW1PW91/6-311G(d,p) level of theory with CPCM chloroform solvation (present as Gaussian formatted output files). The conformational ensemble average 13C and 1H shielding tensor was computed from Boltzmann factors at 298K using the electronic energies (present as text files).
DOI: 10.6084/m9.figshare.793816.v3
title: White Paper on DOE-HEP Accelerator Modeling Science Activities

description: Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within DOE-HEP, and (b) increasing the community-wide coordination and integration of code development.
DOI: 10.6084/m9.figshare.791633
title: Invertebrate feeding (choice test)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791634
title: Leaf litter chemistry and invertebrate feeding data

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin) and invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791705.v9
title: Computational Structure Reassignment of Laurefurenynes A and B

description: The originally assigned stereostructures of laurefurenynes A and B have been reassigned on the basis of DFT calculations of NMR chemical shifts. The deposited files are outputs from geometry optimizations and subsequent calculations of shielding tensors in CPCM, for each of the putative diastereomers. The correct structure is number 6, as described in the paper (http://onlinelibrary.wiley.com/doi/10.1002/chem.201302349/pdf) The data was generated as follows: For each distereomer a 10,000 step Monte Carlo Multiple Minimum (MCMM) conformational search was performed using the MMFF force field. Resulting molecular mechanics geometries within 10 kJ/mol of the global energy minimum were reoptimized with Gaussian09 at the wB97XD/6-31G(d) level with CPCM chloroform. Following these DFT optimizations duplicate conformers were removed to give 2733 conformations across all diastereomers (present as Gaussian formatted output files). For each DFT optimized conformer, shielding tensors were computed at the GIAO-mPW1PW91/6-311G(d,p) level of theory with CPCM chloroform solvation (present as Gaussian formatted output files). The conformational ensemble average 13C and 1H shielding tensor was computed from Boltzmann factors at 298K using the electronic energies (present as text files).
DOI: 10.6084/m9.figshare.791705.v8
title: Computational Structure Reassignment of Laurefurenynes A and B

description: The originally assigned stereostructures of laurefurenynes A and B have been reassigned on the basis of DFT calculations of NMR chemical shifts. The deposited files are outputs from geometry optimizations and subsequent calculations of shielding tensors in CPCM, for each of the putative diastereomers. The correct structure is number 6, as described in the paper (http://onlinelibrary.wiley.com/doi/10.1002/chem.201302349/pdf) The data was generated as follows: For each distereomer a 10,000 step Monte Carlo Multiple Minimum (MCMM) conformational search was performed using the MMFF force field. Resulting molecular mechanics geometries within 10 kJ/mol of the global energy minimum were reoptimized with Gaussian09 at the wB97XD/6-31G(d) level with CPCM chloroform. Following these DFT optimizations duplicate conformers were removed to give 2733 conformations across all diastereomers (present as Gaussian formatted output files). For each DFT optimized conformer, shielding tensors were computed at the GIAO-mPW1PW91/6-311G(d,p) level of theory with CPCM chloroform solvation (present as Gaussian formatted output files). The conformational ensemble average 13C and 1H shielding tensor was computed from Boltzmann factors at 298K using the electronic energies (present as text files).
DOI: 10.6084/m9.figshare.793816.v2
title: White Paper on DOE-HEP Accelerator Modeling Science Activities

description: Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within DOE-HEP, and (b) increasing the community-wide coordination and integration of code development.
DOI: 10.6084/m9.figshare.791631
title: Invertebrate feeding (control data)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791632
title: Invertebrate feeding (no-choice test)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791633.v1
title: Invertebrate feeding (choice test)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791634.v4
title: Leaf litter chemistry and invertebrate feeding data

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin) and invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791631.v1
title: Invertebrate feeding (control data)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791632.v1
title: Invertebrate feeding (no-choice test)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791630
title: Leaf litter chemistry (phosphorus)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791634.v3
title: Leaf litter chemistry and invertebrate feeding data (Dray et al. PLOS ONE)

description: Datafiles to accompany Dray et al PLOS ONE. Leaf chemical composition (carbon and nitrogen, phosphorus, lignin) and invertebrate feeding data (controls, choice, no-choice).
DOI: 10.6084/m9.figshare.791628
title: Leaf litter chemistry (carbon and nitrogen)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791629
title: Leaf litter chemistry (lignin)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791630.v1
title: Leaf litter chemistry (phosphorus)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791628.v1
title: Leaf litter chemistry (carbon and nitrogen)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791629.v1
title: Leaf litter chemistry (lignin)

description: Datafiles to accompany Dray et al. 2014 (DOI: 10.1371/journal.pone.0086246). Leaf chemical composition (carbon and nitrogen, phosphorus, lignin).
DOI: 10.6084/m9.figshare.791611
title: Mac tree with 16 flushes

description: Flush16.bmp model ceated with the base model
DOI: 10.6084/m9.figshare.791611.v1
title: Mac tree with 16 flushes

description: Flush16.bmp model ceated with the base model
DOI: 10.6084/m9.figshare.791617.v1
title: A case of mistaken identity: Using FISC results to examine Type 1 errors in field recording

description: A presentation given to the Botanical Society of the British Isles at their Annual Recorders Meeting 2013.
DOI: 10.6084/m9.figshare.791601
title: CMU-50 Test Set (not finalized)

description: This is an averaged template of a 257 direction DSI dataset that has been reconstructed using QSDR for use in DSI Studio. This is a test run and is not guaranteed to be ready for public use yet.
DOI: 10.6084/m9.figshare.791603
title: History of Ichthyology/ Why Fish are fun (PDF version)

description: My lecture on the history of Ichthyology as well as why fish are important to study, now provided in pdf format for those who are not working on Keynote
DOI: 10.6084/m9.figshare.791601.v1
title: CMU-50 Test Set (not finalized)

description: This is an averaged template of a 257 direction DSI dataset that has been reconstructed using QSDR for use in DSI Studio. This is a test run and is not guaranteed to be ready for public use yet.
DOI: 10.6084/m9.figshare.791603.v1
title: History of Ichthyology/ Why Fish are fun (PDF version)

description: My lecture on the history of Ichthyology as well as why fish are important to study, now provided in pdf format for those who are not working on Keynote
DOI: 10.6084/m9.figshare.791593
title: Timeline 1979-1987

description: A timeline created to provide context for studying Zandra Rhodes dress 1979-1987. 150 of these events are included in a Dipity timeline which can be copied and edited by creating a Dipity account and then choosing this URL to base a timeline upon: http://www.dipity.com/mgramstadt/1979-1987/ This file was created from research undertaken for an article for the journal Costume and funded with support from the University for the Creative Arts.
DOI: 10.6084/m9.figshare.791594
title: Zandra Rhodes Press Cuttings 1979-1987

description: The press books have formed a rich visual and textual source for this research, containing UK and international magazine and newspaper clippings. All the available magazine clippings for the period 1979-87 were consulted, and most of the newspaper clippings as well. The newspaper clippings are held in extremely large and heavy books. The magazine clippings are found in a number of different volumes as well as loose in box files. The research process was to identify any relating to the period 1979-87 as well as checking years around those dates for any miscellaneous items. Using a notebook and by taking low resolution digital images (for reference and to save research time), the author entered all the available information into a spreadsheet. The key resource was visual, but the text in the press cuttings (when set in the correct context and checked for accuracy) was also extremely useful, unfortunately rights issues make it difficult to share those datasets. This spreadsheet may be amended in the future with additional information when possible.
DOI: 10.6084/m9.figshare.791586
title: Algorithm for Multi-Source Deep Transfer Learning

description: The basic architecture of a deep transfer algorithm with separation of concerns. The algorithm is feed some data <em>D</em>, has some bank of prior knowledge in <strong>D</strong>, and relies on two components: a standard machine learning algorithm to analyze the data, and an agent to build an informative prior and/or set the hyperparameters. In this particular case, supervised learning is done with Gaussian processes, but it could also use Support Vector Machines (setting the parameters) or any other supervised learning algorithm. We are looking for the best model given our data and our bank of prior data-sets. In this case, the agent's role is to establish the prior, i.e.: to create a bias toward more likely functions, and choose the hyper-parameters for Gaussian inference. Modelling with Gaussian processes requires a few free parameters (the hyperparameters) and the agent learn to select them. To search efficiently, the agent (reinforcement learning) will use natural language processing, read the labels in the data-sets, i.e.: x1 = humidity, x2 = Linux distribution, and learn to exploit this information to establish the best informative prior. Unlike other deep transfer algorithms like TAMAR, this approach can deal with an arbitrarily high number of sources and has no fixed method of performing transfer: it learns to do it. Reinforcement learning relies on rewards, in this case the reward will be established by the errors of the model during cross-validation and generalization, and how well it performs against a non-informative prior (if available). It should be possible to also tests agents against each other (i.e.: each with a different supervised learning algorithm). An important tool used to exploit the information in the label will be semantic clustering (unsupervised learning), which should clusters similar variables together and help the agent learn how to perform effective transfer.
DOI: 10.6084/m9.figshare.791582
title: Turtle

description: We present a novel method that balances time, space and accuracy requirements to efficiently extract frequent k-mers even for high coverage libraries and large genomes such as human. Our method is designed to minimize cache-misses in a cache-efficient manner by using a Pattern-blocked Bloom filter to remove infrequent k-mers from consideration in combination with a novel sort-and-compact scheme, instead of a Hash, for the actual counting. While this increases theoretical complexity, the savings in cache misses reduce the empirical running times. A variant can resort to a counting Bloom filter for even larger savings in memory at the expense of false negatives in addition to the false positives common to all Bloom filter based approaches. A comparison to the state-of-the-art shows reduced memory requirements and running times.
DOI: 10.6084/m9.figshare.791593.v3
title: Timeline 1979-1987

description: A timeline created to provide context for studying Zandra Rhodes dress 1979-1987. 150 of these events are included in a Dipity timeline which can be copied and edited by creating a Dipity account and then choosing this URL to base a timeline upon: http://www.dipity.com/mgramstadt/1979-1987/ This file was created from research undertaken for an article for the journal Costume and funded with support from the University for the Creative Arts.