cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f2qv3
title: CCDC 2236955: Experimental Crystal Structure Determination

description: Related Article: Xiao-Long Yan, Xiao-Qin Cao, Cheng-Rui Deng, Teng-Fei Zheng, Shu-Li Yao, Sui-Jun Liu|2023|CrystEngComm|25|2366|doi:10.1039/D3CE00130J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dpgdz
title: CCDC 2225161: Experimental Crystal Structure Determination

description: Related Article: Mingoo Jin, Satsuki Matsuura, Hikaru Yamamoto, Motohiro Mizuno, Hajime Ito|2023|Eur.J.Org.Chem.|26|e202201468|doi:10.1002/ejoc.202201468
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f603s
title: CCDC 2240094: Experimental Crystal Structure Determination

description: Related Article: Renè Hommelsheim, Sandra Bausch, Robin van Nahl, Jas S. Ward, Kari Rissanen, Carsten Bolm|2023|Green Chemistry|25|3021|doi:10.1039/D3GC00442B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dz4q8
title: CCDC 2233510: Experimental Crystal Structure Determination

description: Related Article: Yoichi Habata, Ayumi Wada, Eunji Lee, Huieyong Ju, Yuki Yoshiba, Hiroki Horita, Jun-ichi Ishii, Mari Ikeda, Shunsuke Kuwahara|2023|CrystEngComm|25|2349|doi:10.1039/D3CE00169E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d2zbt
title: CCDC 2207396: Experimental Crystal Structure Determination

description: Related Article: Chihiro Iwasaki, Ken Hishinuma, Tsugiko Takase, Dai Oyama|2023|Heliyon|9|e14876|doi:10.1016/j.heliyon.2023.e14876
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d2zdw
title: CCDC 2207398: Experimental Crystal Structure Determination

description: Related Article: Chihiro Iwasaki, Ken Hishinuma, Tsugiko Takase, Dai Oyama|2023|Heliyon|9|e14876|doi:10.1016/j.heliyon.2023.e14876
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22684888.v1
title: Untitled Item

description: Aerosol particals, Sum frequency generation, Atmosph chemistry
DOI: 10.5281/zenodo.7837255
title: Analisis de Mercado: Eroski

description: This dataset contains information about specific products sold by the online supermarket Eroski. It is built based on information scraped by the webpage of Eroski. By making use of the library Selenium, we extracted information about certain type of products in a list, in particular: "bacalao" and "gula". We extracted: product name, price, price per kilo, internal code, categories and URLs.
DOI: 10.5281/zenodo.7837256
title: Analisis de Mercado: Eroski

description: This dataset contains information about specific products sold by the online supermarket Eroski. It is built based on information scraped by the webpage of Eroski. By making use of the library Selenium, we extracted information about certain type of products in a list, in particular: "bacalao" and "gula". We extracted: product name, price, price per kilo, internal code, categories and URLs.
DOI: 10.5281/zenodo.7859477
title: Analisis de Mercado: Eroski

description: This dataset contains information about specific products sold by the online supermarket Eroski. It is built based on information scraped by the webpage of Eroski. By making use of the library Selenium, we extracted information about certain type of products in a list, in particular: "bacalao" and "gula". We extracted: product name, price, price per kilo, internal code, categories and URLs.
DOI: 10.48420/20641017
title: Research data for "Subjective data models in bioinformatics: Do wet-lab and computational biologists comprehend data differently?"

description: Subjective data models dataset <br> This dataset is comprised of data collected from study participants, for a study into how people working with biological data perceive data, and whether or not this perception of data aligns with a person's experiential and educational background. We call the concept of what data looks like to an individual a "subjective data model". <br> Todo: link paper/preprint once published. <br> Computational python analysis code: https://doi.org/10.5281/zenodo.7022789 and https://github.com/yochannah/subjective-data-models-analysis <br> Files <br> **Transcripts** of the recorded sessions are attached and have been verified by a second researcher. These files are all in plain text .txt format. Note that participant 3 did not agree to sharing the transcript of their interview. **Interview paper files** This folder has digital and photographed versions of the files shown to the participants for the file mapping task. Note that the original files are from the NCBI and from FlyBase. Videos and stills from the recordings have been deleted in line with the Data Management Plan and Ethical Review. `anonymous_participant_list.csv` shows which files have transcripts associated (not all participants agreed to share transcripts), what the order of Tasks A and B were, the date of interview, and what entities participants added to the set provided (if any). See the paper methods for more info about why entities were added to the set. `cards.txt` is a full list of the cards presented in the tasks. `background survey` and `background manual annotations` are the select survey data about participant background and manual additions to this where necessary, e.g. to interpret free text. `codes.csv` shows the qualitative codes used within the transcripts. `entry_point.csv` is a record of participants' identified entry points into the data. `file_mapping_responses` shows a record of responses to the file mapping task. <br>
DOI: 10.48420/20641017.v1
title: subjective-data-models-data-deposit

description: # Subjective data models dataset <br> This dataset is comprised of data collected from study participants, for a study into how people working with biological data perceive data, and whether or not this perception of data aligns with a person's experiential and educational background. We call the concept of what data looks like to an individual a "subjective data model". <br> Todo: link paper/preprint once published. <br> Analysis code: https://doi.org/10.5281/zenodo.7022789 and https://github.com/yochannah/subjective-data-models-analysis <br> ## Files <br> - **Transcripts** of the recorded sessions are attached and have been verified by a second researcher. These files are all in plain text .txt format. Note that participant 3 did not agree to sharing the transcript of their interview. - **Interview paper files** This folder has digital and photographed versions of the files shown to the participants for the file mapping task. Note that the original files are from the NCBI and from FlyBase. - Videos and stills from the recordings have been deleted in line with the Data Management Plan and Ethical Review. - `anonymous_participant_list.csv` shows which files have transcripts associated (not all participants agreed to share transcripts), what the order of Tasks A and B were, the date of interview, and what entities participants added to the set provided (if any). See the paper methods for more info about why entities were added to the set. - `cards.txt` is a full list of the cards presented in the tasks. - `background survey` and `background manual annotations` are the select survey data about participant background and manual additions to this where necessary, e.g. to interpret free text. - `codes.csv` shows the qualitative codes used within the transcripts. - `entry_point.csv` is a record of participants' identified entry points into the data. - `file_mapping_responses` shows a record of responses to the file mapping task. <br>
DOI: 10.48420/20641017.v2
title: Research data for "Subjective data models in bioinformatics: Do wet-lab and computational biologists comprehend data differently?"

description: Subjective data models dataset <br> This dataset is comprised of data collected from study participants, for a study into how people working with biological data perceive data, and whether or not this perception of data aligns with a person's experiential and educational background. We call the concept of what data looks like to an individual a "subjective data model". <br> Todo: link paper/preprint once published. <br> Computational python analysis code: https://doi.org/10.5281/zenodo.7022789 and https://github.com/yochannah/subjective-data-models-analysis <br> Files <br> **Transcripts** of the recorded sessions are attached and have been verified by a second researcher. These files are all in plain text .txt format. Note that participant 3 did not agree to sharing the transcript of their interview. **Interview paper files** This folder has digital and photographed versions of the files shown to the participants for the file mapping task. Note that the original files are from the NCBI and from FlyBase. Videos and stills from the recordings have been deleted in line with the Data Management Plan and Ethical Review. `anonymous_participant_list.csv` shows which files have transcripts associated (not all participants agreed to share transcripts), what the order of Tasks A and B were, the date of interview, and what entities participants added to the set provided (if any). See the paper methods for more info about why entities were added to the set. `cards.txt` is a full list of the cards presented in the tasks. `background survey` and `background manual annotations` are the select survey data about participant background and manual additions to this where necessary, e.g. to interpret free text. `codes.csv` shows the qualitative codes used within the transcripts. `entry_point.csv` is a record of participants' identified entry points into the data. `file_mapping_responses` shows a record of responses to the file mapping task. <br>
DOI: 10.15472/anmswd
title: Registros Biológicos de abejas euglosinas priorizadas para evaluación de riesgo de extinción 2022

description: I2D-BIO_2022_136. En el convenio suscrito entre el Instituto de Recursos Biológicos Alexander von Humboldt y el Jardín Botánico de Medellín, mediante el proyecto BIDCA2020-047-USE: Datos basados en colecciones para acciones de conservación, se seleccionaron 50 especies de abejas euglosinas visitantes de plantas epífitas para la evaluación de la categoría de amenaza respectiva. Las 50 especies de abejas euglosinas fueron priorizadas bajo los siguientes criterios: 1. Presencia en Colombia, 2. Confirmación de plantas epífitas visitadas. 3. Especies de interés por sus aspectos ecológicos. Cabe resaltar que los 3 criterios no se cumplen para todas las especies, por tal motivo, se estableció como requisito que cumplieran por lo menos dos de estos para ser evaluadas. En total se incluyen 25547 registros biológicos que incluyen 22 especies del género Eufriesea, 14 del género Euglossa, 9 del género Eulaema, 4 especies del género Exaerete y 1 del género Aglae. Los datos de este recurso se encuentran disponibles en http://i2d.humboldt.org.co/ceiba/resource.do?r=rrbb_abejas_vfloral_2022 y datos de los ejemplares depositados en la Colección de Entomología del Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-E) se encuentran también disponibles en: http://i2d.humboldt.org.co/ceiba/resource.do?r=colecicon_entomologica
DOI: 10.5281/zenodo.7855367
title: NGC - Catalog

description: NGC - Catalog from Carlos Toro and Sergio Barreiro Pérez for PRA1, matter Tipología y ciclo de vida de los datos
DOI: 10.5281/zenodo.7855366
title: NGC - Catalog

description: NGC - Catalog from Carlos Toro and Sergio Barreiro Pérez for PRA1, matter Tipología y ciclo de vida de los datos
DOI: 10.26036/cnp0003411
title: Genome assembly of Fujian oyster

description: Three sequencing and assembly technologies were integrated to perform the de novo assembly. Firstly, the genome of C. angulata was sequenced with 64-fold coverage of 150 bp Illumina paired-end sequencing reads to estimate the genome size and complexity. The C. angulata genome has a high level of heterozygosity (2.6%), and the genome size was estimated at 548-megabase (Mb) (supplementary fig. 1B). Then, 76.3-fold coverage of HiFi reads from the Pacific Biosciences Sequel II system were assembled into 372 contigs with a contig N50 of 12 Mb. Lastly, 97.7-fold coverage of the Hi-C library reads was employed to help generate a chromosome-level assembly, and the final genome assembly of C. angulata comprised 624 Mb with scaffold N50 of 60 Mb.
DOI: 10.35077/act-ski
title: Neuron Morphology data in tiff stack format from Patch-seq experiments in human neocortex, Cell ID 848636884

description: Human neocortical neurons display diverse morphology, physiology and gene expression profiles. We use a method called Patch-seq, where these three data modalities are assayed simultaneously from the same neuron, to understand how the genetic profile of human neocortical neuron types corresponds to their morphological and electrophysiological phenotypes. This collection may contain image stacks (TIFF), reconstruction files in image space (.swc, µm), a transformed file (.swc, µm) oriented so that the y axis extends vertically towards the pia surface, and a marker file (.marker, µm) indicating nodes where dendrites were truncated (10), reconstruction was not continued (20), or surface of tissue was closest to soma (30).
description: https://www.protocols.io/view/patch-seq-recording-and-extraction-detailed-protoc-5qpvoyb2bg4o/v2, https://www.protocols.io/view/3d-reconstruction-of-neurons-in-vaa3d-v-3-36wgq5683gk5/v3
description: DATASET METADATA: title:'Neuron Morphology data in tiff stack format from Patch-seq experiments in human neocortex, Cell ID 848636884', GeneralModality:'cell morphology', Technique:'Patch-seq' SPECIMEN METADATA: LocalID:'H19.06.352', Species:'Human', NCBITaxonomy:'NCBI:txid9606', Age:'26', Ageunit:'years', Sex:'Male', Genotype:'culture, enhancer AAV', OrganName:'Brain', SampleLocalID:'848636884', Atlas:'Ding, Song-Lin et al. “Comprehensive cellular-resolution atlas of the adult human brain.” The Journal of comparative neurology vol. 524,16 (2016): 3127-481. doi:10.1002/cne.24276', Locations:'MTG' INSTRUMENT METADATA: MicroscopeType:'Brightfield', MicroscopeManufacturerAndModel:'Zeiss AxioImager Z2 microscope', ObjectiveImmersion:'Oil' IMAGE METADATA: xAxis:'NA', yAxis:'NA', zAxis:'NA', Number:'NA', displayColor:'NA', stepSizeX:'0.114', stepSizeY:'0.114', stepSizeZ:'0.44'
DOI: 10.5281/zenodo.7859417
title: Precios del mundo

description: Dataset obtenido mediante técnicas de scraping para la asignatura de <em>Tipología y ciclo de vida de los datos </em>de la <em>UOC</em> Los datos se han obtenido del sitio web preciosmundi.com propiedad de Víctor Rodríguez Obensa.
DOI: 10.5281/zenodo.7859418
title: Precios del mundo

description: Dataset obtenido mediante técnicas de scraping para la asignatura de <em>Tipología y ciclo de vida de los datos </em>de la <em>UOC</em> Los datos se han obtenido del sitio web preciosmundi.com propiedad de Víctor Rodríguez Obensa.
DOI: 10.6084/m9.figshare.22684888
title: Comments Aerosols

description: Aerosol particals, Sum frequency generation, Atmosph chemistry
DOI: 10.6084/m9.figshare.22684888.v3
title: Comments Aerosols

description: Aerosol particals, Sum frequency generation, Atmosph chemistry
DOI: 10.6084/m9.figshare.22684888.v2
title: Comments Aerosols

description: Aerosol particals, Sum frequency generation, Atmosph chemistry
DOI: 10.25573/data.22505938.v1
title: Data for Songbirds initiate migratory flights synchronously relative to civil dusk

description: Data for published paper in Movement Ecology "Songbirds initiate migratory flights synchronously relative to civil dusk". <br> These three datasets include all data necessary to recreate results and figures in the paper. Variable explanations for each are below. <br> <strong>Net_Displacement.Rdata</strong> Motus_First_Tag_ID = unique identifier for each individual Species_Season = identifier for species and season observed Mig_Time = time elapsed in hours between detection and departure Net_Displacement_Dist_Neg = distance in kilometers between detection site and departure site Shape = variable needed for ggplot plotting <br> <strong>Departure_Time.Rdata</strong> Species_Season = identifier for species and season observed Spring_or_Fall_Departure = Season of departure Year = year of departure Age = Age at departure (HY = hatch year, AHY = after hatch year, SY = second year, ASY = after second year) Sex = sex of individual Departure_DateTime_UTC = departure date and time in UTC time zone Sunset = time of local sunset in UTC Dusk = time of local civil dusk in UTC Min_After_Sunset = time of departure in minutes elapsed since sunset Min_After_Dusk = time of departure in minutes elapsed since dusk SunAngle_Degrees = sun elevation angle at departure Motus_First_Tag_ID = Motus Tag ID for each individual ID = unique ID of each individual <br> <strong>Clouds_and_Departure_Time.Rdata</strong> Year = year of departure Species_Season = identifier for species and season observed Min_After_Dusk = time of departure in minutes elapsed since dusk Tot_Cloud_Cover.HrOf.31km = estimated total cloud cover for the hour of civil dusk Overcast = binary overcast (&gt;= 95%) vs. mostly clear (&lt;= 25%) variable
DOI: 10.25573/data.22505938
title: Data for Songbirds initiate migratory flights synchronously relative to civil dusk

description: Data for published paper in Movement Ecology "Songbirds initiate migratory flights synchronously relative to civil dusk". <br> These three datasets include all data necessary to recreate results and figures in the paper. Variable explanations for each are below. <br> <strong>Net_Displacement.Rdata</strong> Motus_First_Tag_ID = unique identifier for each individual Species_Season = identifier for species and season observed Mig_Time = time elapsed in hours between detection and departure Net_Displacement_Dist_Neg = distance in kilometers between detection site and departure site Shape = variable needed for ggplot plotting <br> <strong>Departure_Time.Rdata</strong> Species_Season = identifier for species and season observed Spring_or_Fall_Departure = Season of departure Year = year of departure Age = Age at departure (HY = hatch year, AHY = after hatch year, SY = second year, ASY = after second year) Sex = sex of individual Departure_DateTime_UTC = departure date and time in UTC time zone Sunset = time of local sunset in UTC Dusk = time of local civil dusk in UTC Min_After_Sunset = time of departure in minutes elapsed since sunset Min_After_Dusk = time of departure in minutes elapsed since dusk SunAngle_Degrees = sun elevation angle at departure Motus_First_Tag_ID = Motus Tag ID for each individual ID = unique ID of each individual <br> <strong>Clouds_and_Departure_Time.Rdata</strong> Year = year of departure Species_Season = identifier for species and season observed Min_After_Dusk = time of departure in minutes elapsed since dusk Tot_Cloud_Cover.HrOf.31km = estimated total cloud cover for the hour of civil dusk Overcast = binary overcast (&gt;= 95%) vs. mostly clear (&lt;= 25%) variable
DOI: 10.25573/data.21514182
title: Data for Atmospheric pressure predicts probability of departure for migratory songbirds

description: Data from Movement Ecology article: Atmospheric pressure predicts probability of departure for migratory songbirds <br> This dataset includes all data necessary to reproduce the departure date and weather analysis in the above paper. For full weather analysis using Cox proportional hazards models, leave dataset as is. To carry out basic descriptive statistics or ANOVA on departure date data, you must first filter the data to include only Departure_Event = 1 (n = 280). These are the actual departure date events. All other rows are weather data from dates prior to the departure event. <br> <strong>Variable explanation:</strong> <br> Motus_Tag_ID = Unique identifiers in Motus Wildlife Tracking System databases <br> Species_Name = Common and scientific species names <br> Species_Season = Species abbreviation, season of departure, and location of departure <br> Spring_or_Fall_Departure = Variable defining season of departure <br> Age = Age class of individual where HY = Hatch Year, AHY = After Hatch Year, SY = Second Year, ASY = After Second Year <br> Sex = Sex of individual where F = female and M = male <br> Departure_Lat = Latitude of departure location in decimal degrees <br> Departure_Lon = Longitude of departure location in decimal degrees <br> Departure_DateTime_UTC = Estimated departure date and time in UTC timezone <br> Start_Time = First day of departure window for weather analysis <br> End_Time = Last day of departure window for weather analysis <br> Departure_Event = Variable signifying on which day the departure event occured for weather analysis <br> Wind_Profit = signed magnitude of wind speed relative to movement direction (m/s) for the hour of departure <br> Pressure_Trend = slope of relationship between atmospheric pressure and time over the 24 hours previous to departure <br> Total_Precipitation = Total precipitation (mm) over the three hours leading up to departure <br> Total_Cloud_Cover = Total cloud cover (0-1) in the hour of departure <br> Departure_Day_of_Year = Departure date converted to day of year (1-365 for non-leap years) <br> Throughout dataset NA = missing and U = Unknown