cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5128509
title: Cyclin-Dependent Kinase-Like 5 (CDKL5); A Target Enabling Package

description: The protein kinase CDKL5 is best known for harbouring loss of function mutations that cause a variant of Rett syndrome, CDKL5 deficiency disorder, that predisposes to seizures and mental retardation. Recent kinome-wide screening has identified CDKL5 as a therapeutic target to ameliorate acute kidney injury, which is a common complication of hospitalisation that can lead to chronic kidney disease. There are currently no proven treatments. We prepared recombinant proteins for the kinase domains of CDKL1, CDKL2, CDKL3 and CDKL5 and solved the structures of these kinases in complex with identified inhibitors at resolutions from 1.5 to 2.4 Å. Overall, the structures capture the kinases with both active and inactive conformations and provide a model to explain the effects of CDKL5 mutation. An <em>in vitro</em> kinase assay showed the importance of a C-terminal αJ helix for the activity of CDKL2 and CDKL3, but not CDKL1 and CDKL5. Functional analyses of the single orthologue in C. elegans CDKL-1 also suggested that CDKL proteins can limit cilia length, which could potentially contribute to the neurological defects in CDKL5 deficiency syndrome. AST-487 and ASC67 present inhibitors of CDKL5 that could be developed for treating acute kidney injury. However, future work is needed to improve selectivity in this drug development.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of June 2021. For more information about TEPs and the TEP Programme, please visit https://www.cmd.ox.ac.uk/resources/teps
DOI: 10.5281/zenodo.5128460
title: Cyclin-Dependent Kinase-Like 5 (CDKL5); A Target Enabling Package

description: The protein kinase CDKL5 is best known for harbouring loss of function mutations that cause a variant of Rett syndrome, CDKL5 deficiency disorder, that predisposes to seizures and mental retardation. Recent kinome-wide screening has identified CDKL5 as a therapeutic target to ameliorate acute kidney injury, which is a common complication of hospitalisation that can lead to chronic kidney disease. There are currently no proven treatments. We prepared recombinant proteins for the kinase domains of CDKL1, CDKL2, CDKL3 and CDKL5 and solved the structures of these kinases in complex with identified inhibitors at resolutions from 1.5 to 2.4 Å. Overall, the structures capture the kinases with both active and inactive conformations and provide a model to explain the effects of CDKL5 mutation. An <em>in vitro</em> kinase assay showed the importance of a C-terminal αJ helix for the activity of CDKL2 and CDKL3, but not CDKL1 and CDKL5. Functional analyses of the single orthologue in C. elegans CDKL-1 also suggested that CDKL proteins can limit cilia length, which could potentially contribute to the neurological defects in CDKL5 deficiency syndrome. AST-487 and ASC67 present inhibitors of CDKL5 that could be developed for treating acute kidney injury. However, future work is needed to improve selectivity in this drug development.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of June 2021. For more information about TEPs and the TEP Programme, please visit https://www.cmd.ox.ac.uk/resources/teps
DOI: 10.5281/zenodo.5126916
title: Urea Transporter B (UT-B; SLC14A1); A Target Enabling Package

description: UT-B is a member of urea transporter family, which consists of two members (UT-A and UT-B). UT-B is primarily a urea channel transporting urea molecules across plasma membrane by concentration gradient. While UT-B is widely expressed, it is particularly important in red blood cells (RBCs). UT-B is associated with improved response to hydroxyurea treatment in sickle cell diseases due to its transport of hydroxyurea into RBCs, and it is the primary antigen for Kidd (Jk) blood group as well as an accessory protein for ABO blood group. UT-B is responsible for urea transport in the vasa recta of kidney, with secondary effect on controlling the urine volume. This makes UT-B a potential target for the development of a new class of diuretics. This TEP presents the structure of UT-B in apo and inhibitor-bound states, determined by crystallography and cryo-electron microscopy, respectively. We have also confirmed the binding of an inhibitor molecule to UT-B <em>in vitro</em> using a biophysical assay. These enable the molecular characterisation of UT-B as an important blood antigen and will also aid further improvement of the inhibitors as a novel therapy for diuretics and potentially neurodegenerative disorders.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of June 2021. For more information about TEPs and the TEP Programme, please visit https://www.cmd.ox.ac.uk/resources/teps
DOI: 10.5281/zenodo.5126879
title: Urea Transporter B (UT-B; SLC14A1); A Target Enabling Package

description: UT-B is a member of urea transporter family, which consists of two members (UT-A and UT-B). UT-B is primarily a urea channel transporting urea molecules across plasma membrane by concentration gradient. While UT-B is widely expressed, it is particularly important in red blood cells (RBCs). UT-B is associated with improved response to hydroxyurea treatment in sickle cell diseases due to its transport of hydroxyurea into RBCs, and it is the primary antigen for Kidd (Jk) blood group as well as an accessory protein for ABO blood group. UT-B is responsible for urea transport in the vasa recta of kidney, with secondary effect on controlling the urine volume. This makes UT-B a potential target for the development of a new class of diuretics. This TEP presents the structure of UT-B in apo and inhibitor-bound states, determined by crystallography and cryo-electron microscopy, respectively. We have also confirmed the binding of an inhibitor molecule to UT-B <em>in vitro</em> using a biophysical assay. These enable the molecular characterisation of UT-B as an important blood antigen and will also aid further improvement of the inhibitors as a novel therapy for diuretics and potentially neurodegenerative disorders.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of June 2021. For more information about TEPs and the TEP Programme, please visit https://www.cmd.ox.ac.uk/resources/teps
DOI: 10.5281/zenodo.5128532
title: Transposable element annotation of Sitophilus oryzae

description: Sitophilus oryzae TE annotation (GFF), and consensus sequences (fasta).
DOI: 10.5281/zenodo.5128548
title: Hull Springs Wetland Water Depth and Water Temperature Data from 2021-05-04 to 2021-06-11

description: <pre># General Metadata for Hull Springs Restored Wetland Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_pressure_trans_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-06-16 by KF ## File Modified * 2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor. ## Description These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Temperature (dC) and Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date.time.adj - the date and time of the pressure measurement that was adjusted to match the barometric pressure measurement from the weather station. * observation - the incremental number of each observation * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp - the temperature of the water (dC) * Pressure - the pressure recorded by the underwater pressure transducer (mmHg) * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) </pre>
DOI: 10.5281/zenodo.5128547
title: Hull Springs Wetland Water Depth and Water Temperature Data from 2021-05-04 to 2021-06-11

description: <pre># General Metadata for Hull Springs Restored Wetland Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_pressure_trans_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-06-16 by KF ## File Modified * 2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor. ## Description These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Temperature (dC) and Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date.time.adj - the date and time of the pressure measurement that was adjusted to match the barometric pressure measurement from the weather station. * observation - the incremental number of each observation * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp - the temperature of the water (dC) * Pressure - the pressure recorded by the underwater pressure transducer (mmHg) * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) </pre>
DOI: 10.34810/data72/1197
title: 01AC_1.MR.0004.0029.2013.03.10.14.06.27.296875.165383862.IMA
DOI: 10.5281/zenodo.5128542
title: Fact-checking Statistical Claims with Tables

description: The surge of misinformation poses a serious problem for fact-checkers. Several initiatives for manualfact-checking have stepped up to combat this ordeal. However, computational methods are needed tomake the verification faster and keep up with the increasing abundance of false information. MachineLearning (ML) approaches have been proposed as a tool to ease the work of manual fact-checking.Specifically, the act of checking textual claims by using relational datasets has recently gained a lot oftraction. However, despite the abundance of proposed solutions, there has not been any formal definitionof the problem, nor a comparison across the different assumptions and results. In this work, we makea first attempt at solving these ambiguities. First, we formalize the problem by providing a generaldefinition that is applicable to all systems and that is agnostic to their assumptions. Second, we definegeneral dimensions to characterize different prominent systems in terms of assumptions and features.Finally, we report experimental results over three scenarios with corpora of real-world textual claims.
DOI: 10.5281/zenodo.5128604
title: Fact-checking Statistical Claims with Tables

description: The surge of misinformation poses a serious problem for fact-checkers. Several initiatives for manualfact-checking have stepped up to combat this ordeal. However, computational methods are needed tomake the verification faster and keep up with the increasing abundance of false information. MachineLearning (ML) approaches have been proposed as a tool to ease the work of manual fact-checking.Specifically, the act of checking textual claims by using relational datasets has recently gained a lot oftraction. However, despite the abundance of proposed solutions, there has not been any formal definitionof the problem, nor a comparison across the different assumptions and results. In this work, we makea first attempt at solving these ambiguities. First, we formalize the problem by providing a generaldefinition that is applicable to all systems and that is agnostic to their assumptions. Second, we definegeneral dimensions to characterize different prominent systems in terms of assumptions and features.Finally, we report experimental results over three scenarios with corpora of real-world textual claims.
DOI: 10.5281/zenodo.5128541
title: Fact-checking Statistical Claims with Tables

description: The surge of misinformation poses a serious problem for fact-checkers. Several initiatives for manualfact-checking have stepped up to combat this ordeal. However, computational methods are needed tomake the verification faster and keep up with the increasing abundance of false information. MachineLearning (ML) approaches have been proposed as a tool to ease the work of manual fact-checking.Specifically, the act of checking textual claims by using relational datasets has recently gained a lot oftraction. However, despite the abundance of proposed solutions, there has not been any formal definitionof the problem, nor a comparison across the different assumptions and results. In this work, we makea first attempt at solving these ambiguities. First, we formalize the problem by providing a generaldefinition that is applicable to all systems and that is agnostic to their assumptions. Second, we definegeneral dimensions to characterize different prominent systems in terms of assumptions and features.Finally, we report experimental results over three scenarios with corpora of real-world textual claims.
DOI: 10.5281/zenodo.5128603
title: Transposable element annotation of Sitophilus oryzae

description: Sitophilus oryzae TE annotation (GFF), and consensus sequences (fasta).
DOI: 10.5281/zenodo.4570414
title: Transposable element annotation of Sitophilus oryzae

description: Sitophilus oryzae TE annotation (GFF), and consensus sequences (fasta).
DOI: 10.5281/zenodo.5128609
title: Hull Springs Wetland Dissolved Oxygen and Water Temperature Data from 2021-06-11 to 2021-07-16

description: <pre># General Metadata for Hull Springs Restored Wetland Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_pressure_trans_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-06-16 by KF ## File Modified * 2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor. ## Description These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Temperature (dC) and Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date.time.adj - the date and time of the pressure measurement that was adjusted to match the barometric pressure measurement from the weather station. * observation - the incremental number of each observation * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp - the temperature of the water (dC) * Pressure - the pressure recorded by the underwater pressure transducer (mmHg) * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) </pre>
DOI: 10.5281/zenodo.5128608
title: Hull Springs Wetland Dissolved Oxygen and Water Temperature Data from 2021-06-11 to 2021-07-16

description: <pre># General Metadata for Hull Springs Restored Wetland Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_pressure_trans_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-06-16 by KF ## File Modified * 2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor. ## Description These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Temperature (dC) and Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date.time.adj - the date and time of the pressure measurement that was adjusted to match the barometric pressure measurement from the weather station. * observation - the incremental number of each observation * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp - the temperature of the water (dC) * Pressure - the pressure recorded by the underwater pressure transducer (mmHg) * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) </pre>
DOI: 10.5281/zenodo.5128628
title: Hull Springs Wetland Conductivity and Water Temperature Data from 2021-05-04 to 2021-06-11

description: <pre># General Metadata for Hull Springs Restored Wetland Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_pressure_trans_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-06-16 by KF ## File Modified * 2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor. ## Description These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Temperature (dC) and Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date.time.adj - the date and time of the pressure measurement that was adjusted to match the barometric pressure measurement from the weather station. * observation - the incremental number of each observation * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp - the temperature of the water (dC) * Pressure - the pressure recorded by the underwater pressure transducer (mmHg) * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) </pre>