cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5222044
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)

description: Publication date:<br> 2021-08-19T02:41:19+02:00 <br> A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: repackage-gbif-backbone.sh:<br> hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:<br> hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502<br> hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:<br> hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4<br> hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:<br> hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2<br> hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)
DOI: 10.5281/zenodo.5219015
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)

description: Publication date:<br> 2022-12-06T07:37:19-06:00 <br> A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (6 Dec 2022), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/current/simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include:<br> 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. repackage-gbif-backbone.log:<br> log of repackaging of GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2022-12-06.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5<br> hash://sha256/50c155f66efb2efba0b8b624f8541e81cbe16a701d420a5073791fb993f72919<br> hash://sha256/9cd7d4c91292d86c726210446cd6fe45602505a7c0ea3b7c4f4f481f85f193ad (uncompressed)<br> hash://sha256/f950dde25cce9ba9cce67caa1c68ce0c99cb31fe2dc9658fec85a987d9f31654<br> hash://sha256/f21c6b90f17c6083fcfb4853f3c581dcc2aadd291691fa128392a205321f420b (uncompressed)<br> hash://sha256/5e0a4d1d2d1cccbdcc6b2c9831fafe61c54eb055f2d13ec40d9ac161889b9f89<br> hash://sha256/f6e477133d0585706ee5522963b204200cb3cd198f011cbf62be0fa8519763b5 (uncompressed)<br>
DOI: 10.5281/zenodo.7405292
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)

description: Publication date:<br> 2022-12-06T07:37:19-06:00 <br> A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (6 Dec 2022), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/current/simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include:<br> 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. repackage-gbif-backbone.log:<br> log of repackaging of GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2022-12-06.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5<br> hash://sha256/50c155f66efb2efba0b8b624f8541e81cbe16a701d420a5073791fb993f72919<br> hash://sha256/9cd7d4c91292d86c726210446cd6fe45602505a7c0ea3b7c4f4f481f85f193ad (uncompressed)<br> hash://sha256/f950dde25cce9ba9cce67caa1c68ce0c99cb31fe2dc9658fec85a987d9f31654<br> hash://sha256/f21c6b90f17c6083fcfb4853f3c581dcc2aadd291691fa128392a205321f420b (uncompressed)<br> hash://sha256/5e0a4d1d2d1cccbdcc6b2c9831fafe61c54eb055f2d13ec40d9ac161889b9f89<br> hash://sha256/f6e477133d0585706ee5522963b204200cb3cd198f011cbf62be0fa8519763b5 (uncompressed)<br>
DOI: 10.25403/upresearchdata.21602544
title: Analysis of family-supportive supervisor behaviours and subjective career success relationship

description: The data collection method used to collect the present data was quantitative. A cross-sectional survey design was used, in other words, data was collected at one point in time using questionnaires. A non-probability convenience sampling strategy was employed. Data was collected by means of Qualtrics, an electronic web-based survey. The survey included the three instruments to measure the study variables as well as the biographical survey Throughout the study, the researcher adhered to the ethical guidelines as stipulated by the University of Pretoria. Ethical approval was obtained from the Department of Human Resource Management at the University of Pretoria. Since the current study was not specific to an organisation, additional permission and clearance from specific organisations were not needed; however, obtaining individual informed consent from all participants was crucial. To ensure that the present study was conducted ethically, and participation was voluntary, participants were allowed to decide whether to participate or not. Once the data collected period was over, the data was downloaded to Microsoft Excel and then statistical analyses were conducted by means of SPSS and JASP software programs. A codebook is attached which clearly explains the different codes that were used in the dataset.
DOI: 10.25403/upresearchdata.21602544.v1
title: Analysis of family-supportive supervisor behaviours and subjective career success relationship

description: The data collection method used to collect the present data was quantitative. A cross-sectional survey design was used, in other words, data was collected at one point in time using questionnaires. A non-probability convenience sampling strategy was employed. Data was collected by means of Qualtrics, an electronic web-based survey. The survey included the three instruments to measure the study variables as well as the biographical survey Throughout the study, the researcher adhered to the ethical guidelines as stipulated by the University of Pretoria. Ethical approval was obtained from the Department of Human Resource Management at the University of Pretoria. Since the current study was not specific to an organisation, additional permission and clearance from specific organisations were not needed; however, obtaining individual informed consent from all participants was crucial. To ensure that the present study was conducted ethically, and participation was voluntary, participants were allowed to decide whether to participate or not. Once the data collected period was over, the data was downloaded to Microsoft Excel and then statistical analyses were conducted by means of SPSS and JASP software programs. A codebook is attached which clearly explains the different codes that were used in the dataset.
DOI: 10.5283/epub.53290
title: Data archive of "Binding time: Investigations on the integration of visual stimulus duration"
DOI: 10.5281/zenodo.7405322
title: Dataset related to Article "A SINGLE-GROUP STUDY ON THE EFFECT OF ONABOTULINUMTOXINA IN PATIENTS WITH CHRONIC MIGRAINE ASSOCIATED TO MEDICATION OVERUSE HEADACHE: PAIN CATASTROPHIZING PLAYS A ROLE"_ submitted

description: THIS DATASET INCLUDES INFORMATION ON HEADACHE FREQUENCY, MEDICATION INTAKE, MIDAS SCORE, AVERAGE PAIN, HIT-6, ASC-12 AND PCS REFERRED TO PATIENTS RECEIVING ONABOTULINUMTOXINA AS PROPHYLAXIS FOR CM-MOH OVER 12 MONTHS.
description: Users must clarify the untended use of datyabhere uploaded
DOI: 10.5281/zenodo.7405321
title: Dataset related to Article "A SINGLE-GROUP STUDY ON THE EFFECT OF ONABOTULINUMTOXINA IN PATIENTS WITH CHRONIC MIGRAINE ASSOCIATED TO MEDICATION OVERUSE HEADACHE: PAIN CATASTROPHIZING PLAYS A ROLE"_ submitted

description: THIS DATASET INCLUDES INFORMATION ON HEADACHE FREQUENCY, MEDICATION INTAKE, MIDAS SCORE, AVERAGE PAIN, HIT-6, ASC-12 AND PCS REFERRED TO PATIENTS RECEIVING ONABOTULINUMTOXINA AS PROPHYLAXIS FOR CM-MOH OVER 12 MONTHS.
description: Users must clarify the untended use of datyabhere uploaded
DOI: 10.14279/depositonce-16384.2
title: Characterization data of reference materials used for phase II of the priority program DFG SPP 3 2005 “Opus Fluidum Futurum – Rheology of reactive, multiscale, multiphase construction materials”

description: The authors gratefully acknowledge the financial support by the German Research Association (DFG), HeidelbergCement AG for their supply of the cement and the limestone powder and LIAPOR for the supply of the calcined clay.
description: In the mentioned document, the characterization data of the reference materials (CEM I 42.5 R, limestone powder, calcined clay and a mixture of these three components) used in the second funding phase of the priority program 2005 of the German Research Foundation (DFG SPP 2005) are presented under the aspects of chemical and mineralogical composition as well as physical and chemical properties. The data were collected based on tests performed by up to eleven research groups involved in this cooperative program. The file contains the results of the following measurements: Oxide composition; Phase contents; True density; Specific surface area (Blaine and BET); Particle size distribution and D-values; Water demand; Setting time; Mechanical strength; Isothermal calorimetry.
DOI: 10.6084/m9.figshare.20485311
title: Relative_photosynthetic-activity-allExp.xlsx

description: This dataset contains relative photosynthetic activity compared to control on algal strains, synthetic communities and natural phototrophic biofilms. <br>
DOI: 10.21957/e8v4-9954
title: Lower-resolution extended-range reforecasts for the period 1980-2020 (47R3)
DOI: 10.6073/pasta/a00ff0a44f3305f17c99d87e6a0f29fd
title: Mohonk Preserve Stream Water Quality Invasive Species and Macroinvertebrate Sampling in from 2017-Present

description: The mission of the Mohonk Preserve is to protect the Shawangunk Mountains region and inspire people to care for, enjoy, and explore their natural world. Among these 8,000 acres are the vernal pools, permanent springs, tributaries, Humpo Marsh, and the Humpo Kill, and parts of the Kleine Kill and Coxing Kill watersheds within the Hudson River Drainage Basin. Not only are the areas around the Shawangunks established habitats for New York State (NYS) protected species, including an Audubon-designated Important Bird Area, but the watershed also encapsulates more than one agricultural land use area, as well as Rondout Creek, which is an important waterway for the New York City water supply. A conservation plan must be implemented in these areas in particular, keeping in line with the Mohonk Preserves goal to conserve the Shawangunk region for both humans and the greater ecosystem within it. Recognizing the immediate and long-term conservation needs of the streams in this region by employing volunteer data collection will be a catalyst to the Preserves understanding of which environmental threats of this area should be prioritized. The StreamWatch citizen science program will be the newest addition to an array of volunteer research areas, which include collection of weather data, phenology observations, monitoring of peregrine falcon breeding activities, and monitoring of fall hawk migration. Using concise stream monitoring protocol designed for volunteer safety and maximum data accuracy, StreamWatch will evaluate water quality using an array of parameters. Following thorough observation and assessment (which will include analyzing appearance and smell of the water, shape of the stream, canopy cover, nearby land uses, recent weather, and presence of riparian vegetation including invasive species) water quality will be evaluated by means of temperature, dissolved oxygen, pH, and turbidity measurements, in addition to a macroinvertebrate count. Width and depth will also be measured, being indirect contributors to stream quality. The StreamWatch protocol aims to follow a simple procedure that will yield accurate and useful data from all of these aforementioned parameters in order to confirm relevant needs for riparian buffers, changes in nearby agricultural practices (some of which directly affect portions of the watershed), protection against invasive species, and any other potential solutions to problems these watersheds may be afflicted by presently or in the future. Volunteer involvement in this stream water sampling process will encourage the concept of stewardship among visitors of the preserve, while simultaneously honing in on and pursuing various conservation goals.
DOI: 10.5281/zenodo.7405434
title: acanthurus_reversus_ragtag_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7405433
title: acanthurus_reversus_ragtag_01.fasta.gz

description: .
DOI: 10.5281/zenodo.4451998
title: EEGlass motor-imagery and resting-state data

description: Pilot acquisition of EEG data during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction. There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files. <strong>Datasets:</strong> Motor Imagery EEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt) Enobio (data: MI_Enobio.set; header: MI_Enobio.fdt) Resting State (eyes-closed) EEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt) Enobio (data: EC_Enobio.set; header: EC_Enobio.fdt) EOG EEGlass EOG Up (EOG_U_EEGlass.set, .fdt) EOG Down (EOG_U_EEGlass.set, .fdt) EOG Left (EOG_U_EEGlass.set, .fdt) EOG Right (EOG_U_EEGlass.set, .fdt) <strong>Pre-processing:</strong> Bandpass filtering: FIR 1-40 Hz Re-referencing: Common average reference (CAR) Channel locations EEGlass [1:Nz; 2:TP9; 3:TP10] Enobio [1:Fpz ; 2:C3; 3:C4; 4:Pz] Details from the pilot study can be found below: A. Vourvopoulos, E. Niforatos, M. Giannakos, 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 647–652. DOI: https://doi.org/10.1145/3341162.3348383
DOI: 10.5281/zenodo.4451999
title: EEGlass motor-imagery and resting-state data

description: Pilot acquisition of EEG data during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction. There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files. <strong>Datasets:</strong> Motor Imagery EEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt) Enobio (data: MI_Enobio.set; header: MI_Enobio.fdt) Resting State (eyes-closed) EEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt) Enobio (data: EC_Enobio.set; header: EC_Enobio.fdt) EOG EEGlass EOG Up (EOG_U_EEGlass.set, .fdt) EOG Down (EOG_U_EEGlass.set, .fdt) EOG Left (EOG_U_EEGlass.set, .fdt) EOG Right (EOG_U_EEGlass.set, .fdt) <strong>Pre-processing:</strong> Bandpass filtering: FIR 1-40 Hz Re-referencing: Common average reference (CAR) Channel locations EEGlass [1:Nz; 2:TP9; 3:TP10] Enobio [1:Fpz ; 2:C3; 3:C4; 4:Pz] Details from the pilot study can be found below: A. Vourvopoulos, E. Niforatos, M. Giannakos, 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 647–652. DOI: https://doi.org/10.1145/3341162.3348383
DOI: 10.6084/m9.figshare.21680480
title: histogram orig data 220311C&220313E.xlsx

description: This data set presents the major element compositions of the volcanic glass in glass inclusions and embayment hosted by phenocryst minerals of the Koya pumice fall deposit of the Kikai caldera. The chemical compositions of the glass were determined with an Energy-Dispersive X-ray Spectroscopy Oxford X-Max 20 on a Scanning Electron Microscope JEOL JMS6610LV of the Geological Survey of Japan. Water contents were calculated based on the stoichiometric balance between oxygen and other elements after the method of Geshi et al. 2021.
DOI: 10.6084/m9.figshare.21680480.v1
title: histogram orig data 220311C&220313E.xlsx

description: This data set presents the major element compositions of the volcanic glass in glass inclusions and embayment hosted by phenocryst minerals of the Koya pumice fall deposit of the Kikai caldera. The chemical compositions of the glass were determined with an Energy-Dispersive X-ray Spectroscopy Oxford X-Max 20 on a Scanning Electron Microscope JEOL JMS6610LV of the Geological Survey of Japan. Water contents were calculated based on the stoichiometric balance between oxygen and other elements after the method of Geshi et al. 2021.
DOI: 10.5281/zenodo.7405501
title: apogonichthyoides_opercularis_decontaminated_gx.fasta

description: .
DOI: 10.5281/zenodo.7405498
title: apogonichthyoides_opercularis_decontaminated_gx.fasta

description: .
DOI: 10.7924/r4j38x43h
title: Juvenile zebra finch syllables for data-driven analysis of development

description: Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.